Inside GNSS Magazine : Off the shelf and into Space, Volume 9 Number 4 July/August 2014

July/August 2014 | Engineering Solutions from the Global Navigation Satellite System Community | www.insidegnss.com

OFF THE SHELF AND INTO

SPACE

ENVIRONMENTAL SENSING | GNSS RevolutionGNSS SDR | A Toolbox for All SystemsWASHINGTON VIEW | New GPS Leadership

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E-news and analysis twice a month, with exclusives by Washington correspondent Dee Ann Divis and Inside GNSS editor Glen Gibbons

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4 InsideGNSS J U L Y / A U G U S T 2 0 1 4 www.insidegnss.com

ON THE COVER

48 GPS Receiver Performance On Board a LEO SatelliteThe Navigation and Occultation eXperimentAndré Hauschild, Markus Markgraf, and Oliver MontenbruckA test and evaluation program demonstrates that a commercial GPS receiver can operate as a spaceborne research tool.

TECHNICAL ARTICLES

36 Environmental SensingA Revolution in GNSS ApplicationsKristine M. Larson, Eric E. Small, John J. Braun, and Valery U. ZavorotnyIncreasingly sophisticated uses of GNSS observables have led to a new era in remote sensing. A team of researchers describe the results of the applications of interferometric reflectometry to measure snow depth, vegetation water content, and soil moisture.

58 A Universal GNSS Software Receiver ToolboxFor Education and ResearchSanjeev GunawardenaGNSS software defined radio receivers and associated research are proliferating rapidly as computer processing power increases and costs decline. The author describes the latest version of a universal GNSS SDR processing toolbox that is distributed as a plug-in for high-level algorithm development.

COLUMNS

22 Washington ViewNew Leaders at the GPS Helm Dee Ann DivisChanges across the board as new threat to spectrum emerges.

TABLE OF CONTENTS

JULY/AUGUST 2014 VOLUME 9 NUMBER 4

TOC BY THE NUMBERS

12 Thinking Aloud

14 360 Degrees15 GNSS Hotspots

ARTICLES22 Washington

View30 GNSS

Solutions32 GNSS &

Geohazards36 Environmental

Sensing48 GPS Receiver

on a LEO Satellite

58 GNSS SDR Toolbox

68 Working Papers: Assessing GNSS Signal Acquisition

DEPARTMENTS80 Industry View82 Advertisers

Index82 GNSS Timeline

Cover photo courtesy of DLR – German Aerospace Center


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DEPARTMENTS

12 Thinking AloudThe Next Big MacGlen Gibbons

14 360 Degrees/ GNSS HotspotsNews from the world of GNSS

80 Industry View

82 Advertisers Index

82 GNSS TimelineCalendar of Events

30 GNSS SolutionsHow do measurement errors propagate into GNSS position estimates?Mark Petovello

68 Working PapersAssessing the Performance of GNSS Signal Acquisition: New Signals and GPS L1 C/A CodeMyriam Foucras, Bertrand Ekambi, Fayaz Bacard, Olivier Julien, and Christophe Macabiau

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www.insidegnss.com J U L Y / A U G U S T 2 0 1 4 InsideGNSS 9


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ENGINEERING SOLUTIONS FROM THE GLOBAL NAVIGATION SATELLITE SYSTEM COMMUNIT Y

July/August 2014 Volume 9/Number 4

EDITORIALEditor & Publisher Glen Gibbons [email protected]

Art Director Gwen RhoadsContributing Editor for “Working Papers”

Günter Hein [email protected]

Contributing Editor for “GNSS Solutions”Mark Petovello [email protected]

Contributing Editor for “Washington View” Dee Ann Divis [email protected]

Contributing Editor for “Brussels View”

Peter Gutierrez [email protected]

Staff Writer/Editor Eliza SchmidkunzTechnical Editor Fiona Walter

Cover Design: Christine WaringWeb Designer/Webmaster Mike Lee

Web Editor Sierra RobinsonCirculation Director Peggie Kegel

ADVERTISING SALES AND BUSINESS DEVELOPMENTDirector Richard Fischer [email protected]

Mobile: 609-240-1590Office: 732-722-7506 [email protected]

PUBLISHED BY GIBBONS MEDIA & RESEARCH LLCManaging Partner Glen Gibbons [email protected]

Director/Partner Eliza Schmidkunz [email protected] Coburg Road No. 233

Eugene, Oregon, 97401-4802 USATelephone: 408-216-7561

Fax: 408-216-7525

Copyright 2014 Gibbons Media & Research LLC. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical (including by Internet, photocopy, recording, or information storage and retrieval), without permission in writing from Gibbons Media & Research. Authorization is granted to photocopy items, with attribution, for internal/educational or personal non-commercial use. For all other uses, contact Glen Gibbons.

INSIDE GNSS (ISSN 1559-503X) (Online version ISSN 2329-2970) is a controlled circulation magazine, published six times a year (bimonthly). Inside GNSS is a registered trademark of Gibbons Media and Research LLC. Postage paid at Lebanon Junction MPO, KY 40150-9998, Mail Permit #473. INSIDE GNSS does not verify any claims or other information in any of the advertisements or technical articles contained in the publication and cannot take responsibility for any losses or other damages incurred by readers in reliance on such content.

Editorial Advisory Council

VIDAL ASHKENAZINottingham Scientific Ltd., Nottingham, United Kingdom

JOHN BETZMITRE Corporation, Bedford, Massachusetts, USA

PASCAL CAMPAGNEFrance Developpement Conseil, Vincennes, France

MARIO CAPORALEItalian Space Agency, Rome, Italy

PER ENGEStanford University, Palo Alto, California, USA

MARCO FALCONEEuropean Space Agency, Noordwijk, The Netherlands

SERGIO GRECOThales Alenia Space, Rome, Italy

JEAN-LUC ISSLERCNES, Toulouse, France

CHANGDON KEESeoul National University, Seoul, Korea

MIKHAIL KRASIL’SHCHIKOVMoscow Aviation Institute, Moscow, Russia

SANG JEONG LEEChungnam National University, Daejon, Korea

JULES MCNEFFOverlook Systems Technologies, Inc., Vienna, Virginia, USA

PRATAP MISRATufts University, Medford, Massachusetts, USA

BRAD PARKINSONStanford University, Palo Alto, California, USA

TONY PRATTProfessor and Consultant, United Kingdom

SERGEY G. REVNIVYKHISS Reshetnev, Zheleznogorsk, Russian Federation

MARTIN RIPPLEÜberDash Pty Ltd, Australia

CHRIS RIZOSUniversity of New South Wales, Sydney, Australia

TOM STANSELLStansell Consulting, Rancho Palos Verdes, California, USA

JACK TAYLORThe Boeing Company, Colorado Springs, Colorado USA

JÖRN TJADENEuropean Space Agency, Noordwijk, The Netherlands

A.J. VAN DIERENDONCKAJ Systems, Los Altos, California, USA

FRANTISEK VEJRAZKACzech Technical University, Prague, Czech Republic

PHIL WARDNavward Consulting. Garland, Texas, USA

CHRISTOPHER K. WILSONVehicle Data Science Corporation, California, USA

LINYUAN XIASun Yat-Sen University, Guangzhou, China

AKIO YASUDATokyo University of Marine Science and Technology, Tokyo, Japan

Subscribe OnlineFREE one-year subscriptions to the print and/or digital versions are available to qualified readers who work in GNSS-related companies, organizations, research institutes, government agencies, and the military services.

You may also change your address, renew, or unsubscribe online:

WWW.INSIDEGNSS.COM/SUBSCRIPTIONSERVICES


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Here’s the coolest “technology-meets-ingenuity-meets-sus-

tainable-economics” story that I’ve heard in a long time: the International Sun-Earth Explorer-3 (ISEE-3) Reboot Project, a crowd-funded rescue mis-sion to repurpose a 36-year-old NASA spacecraft.

Operating out of an abandoned Mc-Donald’s restaurant near NASA’s Ames Research Center in California, a team led by former NASA employee Keith Cowing and “space technologist” Den-nis Wingo cut a deal with the U.S. space agency. The group would try to wake up the ISEE-3’s onboard systems, refire its engines, bring the craft back into Earth orbit, and put the ancient mariner to work on new tasks.

That’s probably the best use of an abandoned McDonald’s hamburger stand that I’ve heard. (Actually, it’s the only one I’ve heard of. Who could imagine a place and people without the need for a Big Mac?)

Perhaps the Golden Arches caught their attention, but in place of the icon-ic curved brandmark, these visionaries saw the arcs of possible trajectories, intersections in space and time.

In any case, after digging through old documents in the basements of retired NASA engineers, the reboot team recreated the command language and began executing their mission. To their surprise they discovered that many of the ISEE-3’s sensors were still operable after all these years and hav-ing absorbed five times their design level of space radiation. And there was even a little gas left in the tank!

Why did they do it, Cowing asked rhetorically in a July 19 New York Timeop-ed article? “First, because we could: Recycling this piece of space hardware seemed cool and fun. And second, be-cause it might generate useful scientific data — and we could take people all over the world along for the ride,” he answered.

For me, the storyline of the ISEE-3 Reboot Project resurrected not only an aging spacecraft, but the flagging narrative of American can-do, imagi-nation, and energy — those natural dynamics of a young country.

But now this nation is middle-aged, has put on some pounds, been around the block more than a few times. Often it seems as if all that Yankee ingenuity has gone to figuring out ways to make as much cash as soon as possible with other people’s money.

So, this could be an ISEE-3 moment for the GPS program, with lessons that could be learned by all the world’s GNSS operators.

Any one who has had a bathroom or kitchen remodel project has come face to face with the unhappy reality that many things aren‘t built as good as they used to be or even available at

any cost. Skill sets and supply lines have disappeared. Components are unreliable and fail quickly.

In many ways, legacy GPS space vehicles have demonstrated the du-rability of ISEE-3 — the latter voyage begun, by the way, the same year as the first GPS satellite was launched. The 2nd Space Operations Squadron recently removed SVN-34 from its pri-mary orbital slot. Launched in October 1993, the satellite far exceeded its 7.5-year design life and remains capable of broadcasting healthy signals.

The far-reaching changes in stake-holder leadership described by Dee Ann Divis in this month’s Washington View column gives us the chance to think outside as well as inside the box. It provides an opportunity to build not merely personal resumes but to rebuild and build out an enterprise that has

in its way transformed modern life as thoroughly as the Internet or mobile communications.

GPS is the best non-military deal this country has gotten since the Na-tional Defense Highway System. But just as the crisis in the Highway Trust Fund has shown, GPS could suffer the same fate of underinvestment — of ideas as well as money.

Despite our recent diminishing expectations for the mean mission duration of products like dishwash-

ers and refrigerators, we need to treat GNSSs as long-term capital assets with a leadership perspective as strategic as the infrastructure and national role that it represents.

That may require us to look beyond American shores for solutions.

We need to recognize that the decisions we are making now are not for ourselves, or at least for ourselves alone, but for our children and our grandchildren. So, our mindset should not be that of quarterly profits and live for today (sha-na-na) but for a future world that will have ever-greater needs for affordable, available, and accurate positioning, navigation, and time.

GLEN GIBBONS, JR.Editor

The Next Big Mac

THINKING ALOUD

The ISEE-3 Reboot Project is probably the best use of an abandoned McDonald’s hamburger stand that I’ve heard of.


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As the Air Force contemplates chang-ing its GPS III prime contractor in

the face of program delays, sources say it is also weighing much broader program changes to help it weather years of con-tinued lean budgets.

Air Force Space Command (AFSPC) on June 4 released a request for sources able to produce up to 22 GPS III space-craft — an effort to assess the industry’s capability to replace both Lockheed Martin, the current prime contractor, and Exelis, the subcontractor respon-sible for the navigation payload. That payload — and con sequently the GPS III satellites themselves — has been sig-nificantly delayed, much to the ire of the Air Force.

The original $1.46-billion contract let in 2008 anticipated the first GPS III launch would occur this year. And, indeed, for the first few years the Lock-heed team was running ahead of sched-ule. However, that launch schedule has now slipped to late 2015. Exelis (formerly ITT Space) had a role in building all of the GPS navigation payloads but could not produce the GPS III version with the new civil GPS L1C signal and multiple legacy signals within the original time-line.

Numerous U.S. aerospace companies responded to the AFSPC solicitation, as well as at least one foreign firm.

“Obviously we want a GPS III that does what its supposed to do, delivered on time,” Lt. Gen. Ellen Pawlikowski said at a press briefing during this year’s National Space Symposium, Defense News reported. Until recently, Paw-likowski headed the AFSPC’s Space and Missile Systems Center (SMC) at Los Angeles Air Force Base home of the GPS Directorate (GP).

SMC/GP intends to create a competi-tive two-phase process for producing up to 22 GPS III satellites after the first eight for which Lockheed has already been funded. In Phase 1 one or two Production Readiness Firm Fixed Price

(FFP) contracts will be awarded in Fis-cal Year 2015 (FY15). The contractor(s) would conduct space vehicle (SV) and navigation payload critical design review (CDR) with demonstrations and qualifi-cation of the SV subsystem boxes.

Phase 2 will be a limited competi-tion between Lockheed Martin and the selected contractor (or contractors) for up to 22 GPS III production SVs. An award for those satellites is anticipated in the FY17/18 timeframe to support a first SV available for launch no later than the first quarter of FY23.

Aerospace and defense experts have diverse opinions about the AF’s inten-tions for the new solicitation. Some see it as merely a shot across Lockheed’s bow to indicate official dissatisfaction with the delays, some as a serious opportu-nity for new entrants into the program, others as cover for a reworked Lockheed design for the GPS IIIs.

Chance of SV Redesign?Although the “sources sought” announcement indicates that military officials are looking for a production con-tractor and not one to tackle a develop-ment program, experts tell Inside GNSSthat broader changes are being weighed.

Everything is in flux, said one well-informed source, who compared the pace and search for solutions to the sort seen during wartime. The GPS III solic-itation had a deadline for responses of less than two weeks after publication of the announcement.

The expert, like the others who dis-cussed the contract situation, spoke on condition anonymity in order to be able to discuss the program freely.

Another source said the Air Force might be considering launching at least some of the GPS satellites without the nuclear detection payload — an idea that has been floated before.

“There is a very strong possibility that a new GPS III without the (Nuclear Detonation Detection System [NDS]) on

360 DEGREES

it could, in fact, be built,” said the expert. What makes it possible, this expert said, is that enough satellites with the NDS pay-load would be on orbit after the 12th GPS III spacecraft is launched to fulfill the nuclear detection mission through 2040 — making it unnecessary to loft more.

“It’s an avenue that’s being explored,” said the expert. They can fly some cheap-er GPS IIIs without NDS for a while, and “if at that point they need to put it back, they can.”

Pulling NDS from some of the GPS III satellites could allow dual launch of those spacecraft — at a significant cost savings. Although the Air Force announced earlier this year that it would not pursue dual launch, the GPS Program Office is keeping the capabil-

Find out more at GNSS Hotspots online!

Portland, Oregon USA

SMART BALL Adidas has designed every official World Cup ball since 1970. And that’s not all! The Adidas Innovation Team in Portland, Oregon, spent 4 years on a smart soccer ball with a “six-axis MEMS accelerometer sensor package” that can detect speed, spin, strike and flight path data and whip it on over to their special GPS app on your iPhone. The app interprets the data for you, coaches you, and keeps a video to show your friends. On sale now for only $299.

Images and Credits1. Rick Dikeman image of a soccer player, Wikimedia commons2. NASA astronaut Steve Swanson captured this view of Cape Canaveral, Florida from the International Space Station on April 14, 2014.3. Harrison’s chronometer H5 (Collection of the Worshipful Company of Clockmakers)4. Screen shot of Google maps comparison of different routes in the Yahoo Labs experiment.5. Apple image of frequent location screen on iPHone.

GPS III Info Request Draws Competitors, Redesign Ideas


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ity in its plan for the spacecraft so that the option is available later if needed, according to this source.

Search for Payload Supplier AlternativesOne of the conditions under the potential new contract is to preclude Exelis from being the navigation payload provider. Other U.S. firms might be able to pro-vide that capability, but sources suggested that the Air Force may be contemplating looking even farther afield.

“I know they’re looking at whether or not they can buy that package from Galileo and modify it enough for GPS,” said one source. “They’ve got a guy actually spend-ing time and effort to do that analysis.”

The American arm of Europe’s Sur-

rey Satellite Technology Ltd. (SSTL), manufacturer of the Galileo navigation payloads, is certainly willing to discuss the opportunity.

“We are very interested in the alter-nate-source GPS III activities and [are] looking to undertake studies and add value to whatever team we might be able,” said Douglas Gueller, the chief operating officer for SSTL’s U.S. subsidiary. His firm has manufacturing facilities in Denver, he said, and is now executing it first missions in the United States, which include pay-loads for the Air Force and JPL.

One U.S. company likely to have the necessary expertise is Ball Aerospace — which would also be happy to step in.

“[We seek] to assist the Air Force with affordability for GPS by leveraging

360 DEGREESGNSS Hotspots

Barcelona, Spain and Torino, Italy

HAPPY ROUTES Yahoo Labs in Spain and researchers in Turin came up with a mapping algorithm to find the most “emotionally pleasant” ways to get around. They used 3.7 million locations in London from Geograph and Street View, then crowdsourced the response to each location using UrbanGems.org. They plotted the most beautiful, quiet or happy routes and compared them to the shortest. Guess what? It takes only 12% more time, on average, to walk in beauty.

Greenwich, United Kingdom

LONGITUDE PRIZE IIThe Longitude Prize of 1714 was won in bits and pieces by John Harrison for his marine chronometer. Some 300 years later, the British Government decided to encourage solutions to 21st century problems using a renewed version of that drawn-out competition. This one is worth £10 million (US$17 million). The royal museums at Greenwich joined in with a new exhibit: Ships, Clocks & Stars,running through January 4, 2015, and featuring the original Longitude Act and Harrison’s five clocks.

Cape Canaveral and Guiana Space Center

ROOM FOR MORE In less than a decade, we’ll have 100+ GNSS satellites in orbit. The U.S. and the E.U. are doing their part with launches this summer. The seventh GPSII-F will go up from Cape Canaveral on July 31/August 1 and two Galileo FOC satellites will head for the skies from Kourou around August 22.

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Beijing, China and Cupertino, California

NATIONAL SECURITYChina’s state broadcaster CCTV called the iPhone OS7 a national security concern on July 11. They interviewed Ma Ding of People’s Public Security University in Beijing, who said the “frequent location” function could provide Apple with confidential information about the economy “or even state secrets.” The expensive iPhone is popular among high-level bureaucrats and business leaders. Apple said they have never allowed access to their servers nor created a backdoor for any agency of any government and “we never will. “

[our] strong navigation payload capabili-ties,” said Roz Brown, Ball media rela-tions manager, in response to a query on the announcement.

Boeing has also confirmed its inter-est in competing for the potential post-deal. As the prime contractor for the GPS Block IIF satellites now being launched, the company is the likely lead contender, sources agreed.

“[We continue] to believe there are affordable low-risk alternate GPS solu-tions, and looks forward to supporting the Air Force in the Sources Sought pro-cess to best meet the future warfighter needs,” said Paula Shawa, spokesperson for the company’s Communications, Space & Intelligence Systems division in a statement.


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Northrop Grumman has also sub-mitted a response to the Air Force, con-firmed spokesman Lon Rains.

Space Systems/Loral, now known as SSL, wants to be counted in, although it has not yet stepped forward officially.

“SSL did not submit an RFI response for GPS 3, but that does not indicate a lack of interest in offering the USAF a low cost commercial bus solution,” said Chuck Cynamon, vice president for business development at SSL Federal in an emailed statement.

“While we do not build precision navigation and timing (PNT) payloads,” Cynamon added, “we have integrated PNT capabilities onto commercial sat-ellites in the past. If the USAF elects to move forward with an alternative acqui-sition strategy, we would be interested in opportunities to offer a commercially-based solution.”

Lockheed Martin and Exelis are hardly out of the picture, however, and have recently had some success with the navigation payload.

“Right now Lockheed Martin is

focused on delivering GPS III satellites that meet all mission requirements at an affordable cost,” said Lockheed Martin spokesperson Chip Eschenfelder. “As with any complex development pro-gram, there are first article technical challenges; in this case, with GPS III, it has been with the new advanced naviga-

tion payload.”E s c h e n f e l d e r

added, “Test data indi-cates we have resolved all known technical issues. Recently, the last major payload subsystem to be test-ed, the Mission Data Unit (MDU) — which is the heart of the pay-load — completed Acceptance Test and was added to the pay-load’s panel.

T h e c ompl e te d navigation payload panel will now under-go final panel-level testing prior to deliv-ery as a completed navigation payload this fall.”

L o c k h e e d h a s closed its Newtown, Pennsylvania, facilities where the GPS III sat-ellite was developed, with about 350 of

those employees relocating to company sites in Sunnyvale, California, and near Denver, Colorado, where components will be manufactured and the satellites will be assembled.

GNSS Contributes to New Seismic Maps, Early Quake Warning SystemUpdated National Seismic Hazard Maps released by the U.S. Geological Survey (USGS) on July 17 indicate a higher level of earthquake risk for the West Coast and some areas of the Midwest and East Coast then previously thought..

An accompanying USGS report, “Documentation for the 2014 Update of the United States National Seismic Haz-ard Maps,” acknowledged the increas-ing role of geodetic data in assessing earthquake risk. These data are derived primarily from more than 1,800 high-accuracy, continuously operating GPS reference stations.

While all states have some poten-tial for earthquakes, 42 of the 50 states have a reasonable chance of experienc-ing damaging ground shaking from an earthquake during the next 50 years. The hazard is especially high along the West Coast, intermountain West, and in several active regions of the central and

360 DEGREES

2014 USGS National Seismic Hazard Map


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eastern United States, such as near New Madrid, Missouri, and near Charleston, South Carolina.

The latest hazard assessment provides additional fuel for an effort to establish a West Coast Earthquake Early Warning (WC-EEW) system. The USGS, along with partners among uni-versities and state and local agencies, has proposed to develop and operate an EEW system called ShakeAlert, for the high-est-risk areas of the United States. The system would leverage current earthquake monitoring capabilities of the Advanced National Seismic System along with the GPS networks.

The early warning system exploits physical characteristics of earthquakes, which generate two main types of waves: primary or P-waves and secondary (S) waves. P-waves travel at high speeds outward from the earthquake epicenter, but rarely cause damage. S-waves travel more slowly but result in more intense ground shaking that causes damage.

By detecting and analyzing the location and magnitude of an earthquake reflected in the P-wave energy, expected ground-shaking levels across a region can be estimated and warnings sent to local populations before more damaging shaking arrives with or after the S-wave. The advanced warning can range from seconds up to more than a minute, depending on the distance an affected area is from the earthquake’s origin.

That may not sound like much of a head-start on preparing for a quake, but it can provide a crucial edge for efforts to mini-mize damage from a quake, says Ken Hudnut, a geophysicist at the USGS Earthquake Science Center in Pasadena, California, and chairman of the GNSS Working Group for the WC-EEW.

Benefits envisioned for ShakeAlert include such efforts as get-ting school children to safety under their desks sooner, operating automatic shut-off valves for utilities and machinery, putting computer systems into a safer state, or switching other automat-ed systems to try to prevent loss of life or damage to property.

Hudnut discusses the role of GNSS in earthquake research and the WC-EEW in greater detail in a feature beginning on page 32.

Capital investment costs for a West Coast EEW system are projected to be $38.3 million, with additional annual main-tenance and operations totaling $16.1 million, according to a recent USGS “Technical Implementation Plan for the ShakeAlert Production System.”

On July 15, the U.S. House Appropriations Committee voted to include $5 million in an appropriations bill for the U.S. Department of the Interior and Environmental Protec-tion Agency, the first time Congress has specifically committed funds to the Early Earthquake Warning System.

Early Warning, Forecast, and PredictionWC-EEW leaders emphasize that the system is not an earth-quake prediction system, but a rapid reporting method for seis-mic activity already under way. It is also distinct from earth-quake forecasts, such as are represented by the seismic hazard maps published by USGS.

“People seem to understandably mix up these concepts,” says Hudnut, who offers the following definitions; forecast (very long-term, around 30 to 50 years), prediction (short-term, a


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matter of days), and early warning (very short-term, measured in seconds). “We can do forecasting and early warning, but unfortunately we cannot do earthquake prediction in a useful way,” he says.

Early warning refers to the ability to detect an earthquake while it is in prog-ress and report to users instantaneously, Hudnut says. The damaging seismic waves travel at about three kilometers per second, so, a system like the WC-EEW can outpace them by sending the messages ahead through a telemetry sys-tem. WC-EEW must make sure the event detection is solid, then measure the mag-nitude accurately. These tasks in addition to some minor processing and telemetry delays can add up to ten seconds.

Hudnut uses the example of a big San Andreas earthquake in southern California with a magnitude 7.8, starting southeast of Palm Springs and rupturing towards Los Angeles. That city could get up to as much as 50 seconds of advance warning from the system, Hudnut says. “That section of the fault is just over 180 kilometers away; so, the damaging S-waves would take 60 sec-onds to arrive and it takes us about 10 sec-onds to get the size accurately and deliver the alert message.”

In contrast to emerging EEW sys-tems operating in realtime, “earthquake prediction” is a “heavily loaded term,” says Hudnut. “People have wanted us to ‘predict’ earthquakes for years, and what they seem to mean is they want several days of warning prior to a Big One, that is, one that causes significant damage at their particular location.”

To successfully issue a real “predic-tion” in this sense, would require the ability to accurately specify magnitude, location, and time. “Doing that in a use-ful way is essentially impossible to do today, and will probably remain so into the very far future,” Hudnut says, add-ing, “I am continually enthused by new technologies like GNSS coming along, as these allow us to discover new things about earthquakes; so, I do not con-sider prediction totally hopeless. But it is clearly a very, very long way off and maybe never attainable.”

Growing Role for GNSSGPS-based geodetic data charts the rate

(velocity) and direction of movement of the strategically sited reference, reflect-ing crustal plate motion and deforma-tion or “strain” of the underlying Earth layers. USGS experts combine the geo-detic data with geologic data and seismic sensors that measure slippage and vibra-tion along fault lines.

“Modern GPS data provide more spa-tially complete observations on ground deformation, cover a period of 25 years (from 1987–2012), and record the most recent plate-loading rates with submil-limeter per year precision, which is more precise than data provided by geologic studies,” the USGS researchers wrote in their report.

“In long-term forecasting, geodesy is useful because we assess the rate of load-ing of active fault zones,” says Hudnut. “We measure the velocity vector very precisely over many years for each of our continuously operating GNSS sta-tions, the Plate Boundary Observatory with over 1,000 GNSS stations and all of the USGS’s over 100 additional GNSS stations along the San Andreas fault sys-tem and across the Los Angeles and San Francisco urban areas.”

These data are combined with other data, such as geological fault slip rates and the USGS seismicity catalog, to make the long-range earthquake hazard assessments or “forecasts.” With better understanding of potential ground shak-ing levels, such forecasting can support various risk analyses by considering factors like population levels, building exposure, and building construction practices.

In turn, these analyses can be used for establishing building codes and estimating seismic risk for key structures. They can also help in determining insurance rates, emergency preparedness plans, and private property owners evaluating their homes in order to make them more resilient.

Communications Act Rewrite Could Adversely Affect GPS CommunityTwo powerful lawmakers are weighing rewriting the rules for the way frequen-cies are allocated as part of an overhaul of the nation’s telecommunications laws. The effort, which is likely to see legis-lation drafted next year, is considering options such as flexible licensing and receiver standards that could directly affect the GPS community.

“It’s been quite some time since there’s been any type of update or a num-ber of hearings on reviewing the Com-munications Act,” said Rep. Fred Upton, R-Michigan, in a taped announcement in December 2013.

“We’re prepared in essence to talk about a launch of a number of hear-ings that we will have next year. . . . Our goal, in fact, will be to use these hearings throughout the course of the next year to begin to actually launch an update beginning in 2015.”

Upton, who chairs the House Energy and Commerce Committee, made the announcement with Rep. Greg Walden, R-Oregon, the chairman of the Com-munications and Technology Subcom-mittee. The two committees oversee the Federal Communications Commission (FCC) and have primary authority over spectrum issues in the House.

Since their announcement, the two con-gressmen have held an FCC oversight hear-ing, another hearing in January on updat-ing the Communications Act of 1934, and issued three white papers for comment in a lead-up to drafting legislation.

One of the points they underscored in the policy papers is how much tech-nology has changed since the communi-

360 DEGREES

See additional news stories at WWW.INSIDEGNSS.COM/NEWS

Galileo IOV Satellite Failure Mystery Still Unsolved

GPS OCX Program Being Restructured as Budget Pressures Mount

Harbinger Sues U.S. Government over LightSquared

FCC Courts GPS Community in Effort to Solve Spectrum Crunch

GNSS Monitoring Stations Slide into U.S.-Russia Rift

Homeland Security Researching GPS Disruptions, Solutions

. . . and more.


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cations law was written and even since its most recent major update.

Spectrum Allocation White PaperThe issue of spectrum allocation, and the need for more frequencies for commer-cial use, seems to be near the top of the work list — deserving of a white paper of its own. The paper asks for feedback on a number of questions including whether the FCC should be allowed to take the potential revenue from spectrum auc-tions into account when making allo-cation decision, something that would likely put additional pressure on the highly desirable L-band, where the GPS frequencies are located.

It also asks about shifting to “flex-ible use” licenses which, the paper said, would “permit licensees to use their spectrum for any service, including wireless, broadcast, or satellite services,” as opposed to the current system of des-ignating what uses can be made of spe-cific frequencies.

The GPS Innovation Alliance noted in its submitted comments that “flex-ible use” is described in another white paper by the FCC Technical Advisory Committee (TAC) as permitting “uses up to and including high power mobile network downlinks.”

“While such a regime provides a great measure of freedom to the licensee who acquires flexible use spectrum,” the Alliance wrote, “this flexibility comes at a cost to any adjacent spectrum hold-er, who will be expected to be able to accommodate the full range of permit-ted operations, up to and including very high powered operations.

If the adjacent band use is not imme-diately compatible with high powered use, the TAC White Paper appears to suggest that adjacent spectrum holders will be forced to accommodate the use over time.”

The TAC assumed in its white paper, “without providing technical evidence,” said the Alliance, “that with the right amount of time and some unknown level of investment in new technology or alternative product design, any spec-trum use should be able to accommo-date high-powered, cellular-like opera-

tions in directly adjacent spectrum. This assumption has not been thoroughly tested and validated, and based upon past instances involving significant inter-ference between dissimilar uses in close spectral proximity, may be unfounded.”

The Alliance suggested a “zoning” and re-farming approach, which groups similar types of services together. This method could reduce the need for spec-

trum by reducing the need for the guard bands, or buffer zones between users, they suggested.

Receiver Standards & Reducing NTIA RoleThe lawmakers also asked about the desirability of setting standards that

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Continued on page 80.


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Washington, D.C., has a peculiarity of sea-sons. While most of the world marks the shifts

between winter and spring, summer and autumn, the politicos on the streets of the U.S. capital count the passage of time in two-year increments.

New operatives and appointees flock to the centers of power in the early days of each administration and the open-ing of each Congress, then migrate to

friendlier climes as congressional elec-tions loom and the administration winds down — as it is now.

The GPS community, in particular, seems to be caught up in this season’s migration. New personnel are stepping into key leadership posts in the depart-ments of defense and transportation as well as other agencies that work together to guide the GPS program.

The National Coordina-tion Office (NCO) for Space-Based Positioning, Naviga-tion, and Timing (PNT), which supports all of these efforts, has been operat-ing for months without a top executive. Changes are occurring in the congressio-nal committees that autho-rize the GPS budget and perhaps in those that can influence the fortunes of the constella-tion’s operations and utility.

These personnel shifts are occurring just as a new effort to rewrite the nation’s telecommunications laws is emerging, potentially threatening GPS frequencies and applications.

The most notable shifts are within the Department of Defense (DoD).

General William Shelton, who became Commander of Air Force Space Com-mand (AFSPC) in January 2011, will retire September 1. By all accounts his leadership of those responsible for the modernization and support of the GPS system has been admirable. Although no job at this level is ever easy, his tenure has weathered one squall after another with years of unusually serious budgetary challenges, glitches in efforts to enhance the system, and the rise of new threats to, and doubts about, the availability of GPS signals.

When he assumed the helm in 2011, Shelton stepped immediately into the middle of what would become the big-gest threat so far to GPS frequencies. That same month a Virginia firm called LightSquared, amid great controversy, won conditional permission from the Federal Communications Commission (FCC) to build a coast-to-coast, high-powered terrestrial broadband network using frequencies neighboring those used by GPS. The firm’s project fit neatly into the Obama administration’s plan to pro-vide more spectrum for broadband com-panies and encourage competition within the wireless communications industry.

RF power from LightSquared’s trans-mitters and mobile dev ices , however, threatened to over-whelm GPS receivers across the country. Shelton took a stand against LightSquared’s aspirations and made nat iona l news for refusing to submit to pressure from the White House to soften congressional testi-

mony about the clear interference prob-lems that testing had shown. Although the issue is not fully resolved, the Light-Squared project is currently sidelined.

Patent, Jamming, and Budget ProblemsOther challenges followed, including an effort by the British Ministry of Defense

New Leaders at the GPS Helm CHANGES ACROSS THE BOARD AS NEW THREAT TO SPECTRUM EMERGES

DEE ANN DIVIS

Dee Ann Divis has covered GNSS and the aerospace industry since the early 1990s, writing for Jane’s International Defense

Review, the Los Angeles Times, AeroSpace Daily and other publications. She was the science and technology editor at United Press International for five years, leaving for a year to attend the Massachusetts Institute of Technology as a Knight Science Journalism Fellow.

WASHINGTON VIEW

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to patent one of the modernized GPS signal structures — a patent that was withdrawn after what experts described as government-to-government discus-sions. Other concerns arose about the aging GPS constellation, repeated dem-onstrations that the GPS signal could be spoofed and jammed, and surging threats from hackers.

Shelton also found himself caught up in budget battles consuming Washington as well as significant problems with the navigation payload on the new GPS III satellites and the new ground system. To tackle both the technical and financial problems, his team weighed a variety of alternatives including reconfiguring the GPS constellation to take advantage of very small satellites and investing in the ability to launch more than one satellite at a time. Among the outcomes of that work is the recent restructuring of the ground system contract and the release in June of a request that could lead to a new prime contractor for the last 22 GPS III spacecraft.

Shelton seized some opportunities as well. He is credited with turning on the navigation message in the L2C and L5 signals, a move expected to spur receiver development and innovation as the com-mercial sector gains experience with the new signals.

New AF Space CommanderShelton will be replaced by Lt. Gen. John Hyten, who has served as his AFSPC vice-commander for the last two years. Hyten has a Harvard engineering degree and a masters in business administration and has served in Washington as direc-tor for space programs in the Office of the Assistant Secretary of the Air Force for Acquisition — experience that should serve him well has as sequestration reemerges and squeezes the GPS budget tighter still.

Hyten’s experience as the direc-tor of space forces during Operations Enduring Freedom and Iraqi Freedom, and in senior engineering positions on anti-satellite weapon system programs, also could prove very valuable as the challenges from adversaries mount. He

also has worked with the GPS program before, noted one source, and has direct experience with the interagency process that guides GPS policy.

“He’s definitely one of the GPS people,” said the well-informed source, who asked not to be identified due to a lack of autho-rization to speak publicly. “We’re glad to have him over in Space Command.”

In yet another change for Space Com-mand, Lt. Gen. Ellen Pawlikowski, the head of the Space and Missile Systems Center at Los Angeles Air Force Base, has left to become military deputy with-

in the Office of the Assistant Secretary of the Air Force for Acquisition — replacing Lt. Gen. Charles Davis as the service’s top military acquisition official. SMC, which is part of Space Command, is home to the Global Positioning Systems Directorate.

Taking over at SMC is Lt. Gen. Sam-uel Greaves, who was serving as deputy director in the Missile Defense Agency’s Office of the Undersecretary of Defense for Acquisition, Technology and Logistics.

Other PNT Leadership ChangesThe handoffs within Space Command are not the only changes in the GPS

management team at the Department of Defense.

Robert Work has replaced Ash-ton Carter as the deputy secretary of defense and takes his place as co-chair of the National Executive Committee for Space-Based Positioning, Navigation, and Timing (PNT ExCom). Work, who served 27 years in the U.S. Marines, was undersecretary of the Navy from 2009 to 2013. He has a master of science degree in space system operations and served on the DoD transition team for the in-coming Obama administration. He was confirmed as deputy secretary in April.

His co-chair on the PNT ExCom is also relatively new to the job. Vic-tor Mendez is acting deputy secretary of transportation. He stepped into the roll in late December 2013 to replace the departing John Porcari. Mendez, who has an MBA and a degree in civil engineering, was sworn in as the federal highway administrator in July 2009. He also served on the White House transi-tion team.

The PNT ExCom is supported by the Executive Steering Group, which

meets more often than the ExCom, and deals with interagency matters that can be handled without elevating them to the deputy secretary level. The Steering Group has representatives from the same departments as the ExCom plus agencies within those departments such as the Air Force and the Federation Aviation Administration.

The Pentagon’s chief information offi-cer (CIO) and the Department of Trans-portation (DoT) assistant secretary for research and technology co-chair the Steering Group. Greg Winfree has held the latter position for a while, but the DoD CIO, whose office is the nexus for

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Lt. Gen. John Hyten, incoming chief of Air Force Space Command

Although no job at this level is ever easy, Shelton’s tenure at AFSPC has weathered one squall after another with years of unusually serious budgetary challenges, glitches in efforts to enhance the system, and the rise of new threats to, and doubts about, the availability of GPS signals.

WASHINGTON VIEW


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DoD decisions on GPS policy and pro-curement, is new. Teri Takai left that job at the beginning of May with only a week’s notice to the surprise of many in the GPS community

“I think she did a lot to establish the influence of the CIO office,” said Scott Burgett, director of automotive OEM platform engineering for GPS user equip-ment manufacturer Garmin Internation-al, who praised her as “a very effective advocate for GPS.”

Takai has been replaced, for now, by Terry Halvorsen, who is the Pentagon’s acting CIO. His role, however, may not be permanent. According to Defense Daily, the DoD has issued a formal state-ment from spokeswoman Lt. Col. Valerie Henderson stating, “Mr. Halvorsen will lead the DoD CIO organization until a permanent DoD CIO is selected by [Defense] Secretary [Chuck] Hagel.”

Halvorsen’s situation underscores the challenges facing anyone in a job in an acting capacity. When so much depends on the skills, expertise, perspectives, and advocacy of individuals, having people who may not be staying — or are per-ceived as temporary — makes planning and progress much harder.

ExCom NewbiesThe departments of state, commerce and homeland security all have representa-tives to both the ExCom and the steering group who are either new to their jobs or still in an acting capacity.

Bruce Andrews was just named acting deputy secretary of commerce in June. His experience in telecommunications could be especially useful as the battle over spectrum heats up. He was general counsel for the Senate Committee on Commerce, Science, and Transportation, the committee that oversees the FCC. Prior to that he was a telecommunica-

tions attorney and managed government affairs for Ford Motor Company.

Commerce is represented in the PNT Executive Steering Group by former astronaut Kathryn Sullivan, who was finally confirmed in March as the under secretary of commerce for oceans and atmosphere and as National Oceanic and Atmospheric Administration (NOAA) administrator. She is an oceanographer who, when she served as NOAA’s chief scientist, oversaw a research and tech-nology portfolio that included fisheries biology, climate change, satellite instru-mentation, and marine biodiversity.

The GPS commercial sector may particularly appreciate the background of Judith Garber, the State Depart-ment’s ExCom representative and the acting assistant secretary of state for the Bureau of Oceans and International Environmental and Scientific Affairs (OES). She is a career foreign service officer who has held economic and busi-ness development posts around the world and was named to her new post at the end of April.

The State Department’s person in the steering group is Jonathan Margolis, acting deputy assistant secretary at OES for the science, space, and health. He has a Ph.D. in psychology from Harvard University, focusing on negotiation and conflict resolution, and a master’s degree from the Fletcher School of International Law and Diplomacy as well as hands-on experience organizing international

communications. Given that the current round of international negotiations over spectrum is coming to a head next year, his skills could be very valuable.

Just joining the PNT ExCom is Deputy Secretary of Homeland Security (DHS) Alejandro Mayorkas. A former U.S. attor-ney, he had been serving as the director of DHS’s United States Citizenship and

Immigration Services (USCIS), which operates the largest immigration system in the world. He was sworn in last December.

Confirmed in March, as under secre-tary for DHS’s National Protection and Programs Directorate, Suzanne Spauld-ing brings a wide range of experience in intelligence and cybersecurity matters. She served at the Central Intelligence Agency for six years and on the staff of both the House and Senate intelligence committees.

Spaulding’s role at DHS gives her the responsibility for protecting critical infra-structure. GPS already is considered an essential element in most of the nation’s critical infrastructure sectors, and some top experts have argued GPS should itself be designated critical infrastructure.

New Leadership at NCOThe leadership at the National Coordi-nation Office, the permanent staff for PNT ExCom, is also in f lux. Former NCO director Jan Brecht-Clark retired in December after serving just a year. The NCO’s deputy director, Col. Harold “Stormy” Martin, retired from military service several months later.

Now that Martin has left the military he could potentially return as the NCO’s director — the path taken by Anthony Russo, who was NCO director from January 2010 through December 2012. The DoT selects the NCO director and is expected to name its choice in early July, although nothing had been announced as of press time.

Meanwhile, on Capitol HillKey personnel changes are also under way in Congress with some members retiring, some moving to different roles because of term limits, and others forced out by election losses.

Among those retiring will be the chairmen of the both the House and Sen-ate armed services committees, which oversee the GPS program. Overall, these committees and their chairmen have been very supportive of GPS, particularly in their budget authorizations.

House Armed Services Chairman Howard “Buck” McKeon, R-California,

When so much depends on the skills, expertise, perspectives, and advocacy of individuals, having people who may not be staying — or are perceived as temporary — makes planning and progress much harder.

WASHINGTON VIEW


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who is retiring from Congress, told reporters that term limits, which man-date his giving up his chairmanship, were the “biggest motivator” for his decision. Rep. Mac Thornberry of Texas, who is described in news reports as being extremely knowledgeable on defense matters and who has already made his interest in the chairmanship clear, is the most likely replacement.

Sen. Carl Levin, D-Michigan, chair of the Senate Armed Services Commit-tee, is also retiring. He has endorsed Sen. Jack Reed, D-Rhode Island, as his suc-cessor, according to the web site Daily Kos, but Reed is also in line to take the soon-to-be-vacant chairmanship of the Senate Banking, Housing and Urban Affairs Committee. Senators Bob Nel-son, D-Florida and Claire McCaskill, D-Missouri, would be next in line by seniority although Nelson has a number of other options.

The leadership situation in two other Senate committees is particularly worth watching. Sen. John D. Rockefeller, D-West Virginia, who now heads the Commerce and Transportation Com-mittee, is retiring after five terms. Mark Pryor, D-Arkansas, the chairman of the Communications, Technology and Inter-net Subcommittee, is in a very close race this fall. These two committees have the lead on spectrum issues in the Senate and played a role in the LightSquared debate a few years ago.

The chairmanships are particularly important in this case because the heads of the corresponding House committees have launched an effort to update the Communications Act of 1934 and have placed issues that could greatly impact GPS squarely on the table.

House Energ y and Commerce Committee Chairman Fred Upton, R-Michigan, and Communications and Technology Subcommittee Chairman Greg Walden, R-Oregon, announced their multi-year review in December. They have asked for feedback on issues including receiver standards and the idea of altering the role the National Telecom-munications and Information Adminis-tration (NTIA).

NTIA watches out for the federal gov-ernment users of frequencies — includ-ing those who rely on GPS — and played a key role in protecting the GPS spec-trum during the LightSquared contro-versy. As things now stand, the FCC and NTIA have to agree on frequency allo-cations, an arrangement deemed dupli-cative by some who would like the FCC to have most if not all of the decision-making power. For more on this story, see news article on page 20.

Upton and Walden appear well posi-tioned and, given that term limits will force Upton to relinquish his chairman-ship by 2017, well motivated to launch legislation next session. Whether they succeed or not depends in part on who chairs the Senate committees. Sen. Bar-bara Boxer, D-California, is next in line for the chairmanship, but she already leads other committees and the Demo-crats have not imposed term limits on their members. Democrats Bill Nelson of Florida and Maria Cantwell of Washing-ton state would seem to be likely choices based on seniority.

In any case, it’s too soon to know, particularly since most political experts give the Republicans better than even odds of taking control of the Senate in this fall’s elections. If that happens, then Republicans will control the chairman-ships and set the agenda. If they can

come to agreement amongst themselves, they will be in a much stronger position to push changes through.

Unfortunately, the GPS community already has lost some of the members who acted to protect GPS frequencies during the LightSquared fracas.

Of the six members that organized “Dear Colleague” letters opposing Light-Squared‘s request in the spring of 2011, half are gone or on their way out. Sen. Ben Nelson, D-Nebraska, and Rep. Steve Austria, R-Ohio, both declined to seek re-election in 2012. Rep. Ralph Hall, R-Texas, the oldest-serving member of Congress, lost his primary bid to a Tea Party challenger this spring. A fourth GPS advocate, Rep. Collin Peterson, D-Minnesota, is more likely than not to win, according to Larry Sabato, an expert on electoral politics at the University of Virginia’s Center for Politics, but he is in a competitive race.

All in all, the GPS community is facing a substantial new challenge over spectrum with a team that is largely new to GPS issues. It also has fewer proven friends on Capitol Hill to speak on its behalf. The good news is that the new contingent of GPS leaders has an array of particularly useful skills and time to plan ahead for the next fight. Whether they will be ready or not remains to be seen.

Rep.Greg Walden, R-Oregon, at Communications Act update event.

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Not surprisingly, GNSS posi-tioning accuracy is largely dependent on the level of

measurement errors induced by orbital inaccuracies, atmospheric effects, mul-tipath, and noise. This article discusses how, specifically, these errors manifest as position errors.

Estimating a PositionFor the purpose of our discussion here, we only consider least-squares estima-tion with no a priori knowledge of the receiver’s position or time. To this end, least-squares assumes the mea-surements, , are related to the states (position and clock bias, in the case of GNSS), , as follows

where h— assumed, for convenience, to be non-linear — and is the vector of measurement errors. This equation is linearized to yield

where is the current estimate of the state vector (point of expansion),

is the error of relative to the (unknown) true states, H is the Jaco-bian matrix (also called the design matrix, observation matrix, or geom-etry matrix), and is the misclosure vector, which is the difference between the true measurements ( ) and the measurements estimated from the cur-rent states (i.e., ).

The well-known solution to equa-tion (2) is as follows:

where R is the covariance matrix of the measurement errors. The initial state estimates are then updated as follows

Because the model is non-linear, we can use iteration to converge to the final solution.

Role of GNSS ErrorsFor the purpose of this article, the pseudorange measurement equation (equivalent to equation [1]) is written as

where is the vector of pseudoranges from all satellites in view, is the vec-tor of geometric distances between the receiver and the satellites, b is the receiver clock error (common across measurements), and is the aggregate measurement error from all error sources. Although we aggregate all of measurement errors together, indi-vidual components (e.g., troposphere) could be separated and easily worked through the following development.

Let us now consider the specific case where the initial state estimate was perfect such that . Although this is an unrealistic scenario (if you knew the true position in advance, you do not need GNSS!), it serves as a useful illustration of how measurement errors affect the final solution. Furthermore, since the least-squares approach will yield the same position estimate for all reasonable initial state estimates (in this case, “reasonable” would include a position accurate to at least 1,000 kilo-meters), this scenario is not limiting.

For the assumed case, the true value of is zero. It follows that if the value estimated from equation (3) differs from zero, this actually represents the

GNSS Solutions is a regular column featuring questions and answers about technical aspects of GNSS. Readers are invited to send their questions to the columnist, Dr. Mark Petovello, Department of Geomatics Engineering, University of Calgary, who will find experts to answer them. His e-mail address can be found with his biography below.

GNSS SOLUTIONS

MARK PETOVELLOis a Professor in the Department of Geomatics Engineering at the University of Calgary. He has been actively involved in many

aspects of positioning and navigation since 1997 including GNSS algorithm development, inertial navigation, sensor integration, and software development. Email: [email protected]

How do measurement errors propagate into GNSS position estimates?


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error in the estimated states. To obtain a more explicit equation, we first com-pute the misclosure vector as follows

In other words, the misclosure vec-tor contains the measurement errors only. Finally, substituting this result into equation (3) gives

Equation (7) shows how measure-ment errors propagate into the final solution. Although the equation is rela-tively simple, there is no hard-and-fast rule for describing how this happens. Rather, we can only say that two key things determine the effect of mea-surement error on the final solution: the relative measurement accuracy reflected in R, and the measurement geometry as reflected in the Jacobian.

Before looking at these aspects in more detail, note that equation (7) shows the effect on all state estimates separately (i.e., as a vector). This is important because some applications may be more interested in certain parameters than in others. For exam-ple, aviation is more sensitive to verti-cal positioning errors than horizontal positioning errors. In contrast, timing applications are not concerned at all with the position states.

Measurement AccuracyIntuitively, the more accurate a mea-surement is assumed to be, the more weight will be given to that measure-ment. As R is the covariance matrix of the measurement errors, this weighting of the measurements happens “auto-matically” within the least-squares estimation process.

Of course, because the user (or perhaps software programmer) is responsible for selecting the covariance model, careful decisions need to be made in this regard; otherwise results will be suboptimal.

Measurement GeometryTo further explain the idea of measure-ment geometry, a single row of the

Jacobian matrix (corresponding to the i-th single measurement) can be writ-ten as

where is the unit vector pointing from the receiver to the i-th satellite. The distribution of all satellites relative to the user reflects the measurement geometry. This is often quantified using dilution of precision (DOP) values.

To illustrate the importance of mea-surement geometry, consider Figure 1,which shows two measurement sce-narios for a two-dimensional position-ing problem. In both cases, the receiver (blue) is measuring ranges (not pseu-doranges) from the transmitters (red). Each transmitter is assumed to have an error of one meter, and all measure-ments are given equal weight (i.e., same variance).

The distribution of transmitters appears to be relatively similar; only one transmitter is moved (mirrored across the y-axis). Nevertheless, this small difference in measurement geometry results in different position errors.

Similar examples can be developed for the three-dimensional case, but this is more complicated to draw and is omitted here.

Unfortunately, users cannot place satellites to optimize measurement geometry. The best that can be done is to use mission-planning utilities to col-lect data during parts of the day where geometry is best (in the area of the data

collection). Of course, using receiv-ers that track satellites from multiple GNSSs will inherently improve the geometry too.

Estimating Clock ErrorsThe examples in the previous section only considered the case of measured ranges, meaning the clock error state does not need to be estimated. Howev-er, estimating the clock error — which is common across all measurements — can significantly affect results.

In particular, although we name the state the “clock error,” the estimated value will include the true clock error along with anything that appears to becommon across all satellites.

With this in mind, if we repeated the previous examples using pseu-doranges (thus requiring the clock error to be estimated), the fact that all measurements were assumed to have a one-meter error means that the least-squares estimator could not separate the true clock bias from the common error. The result would be that the clock error estimate would be biased by one meter (in this case), but the posi-tion error would actually be zero!

Different Types of ErrorsAlthough equation (7) completely defines the propagation of a specific set of errors (i.e., at a particular instant of time) from the measurement domain to the position (and time) domain, this equation is usually reserved for system-atic errors that manifest as biases in the

FIGURE 1 Example of the role of measurement geometry. These two examples assume ranges are measured to each transmitter and each has an error of 1 meter. Despite the similar geometry, the resulting position errors are quite different.

δx = 0.85 mδy = 0.85 m

δx = 0.64 mδy = 1.35 m

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For at least two decades, GPS experts, geodesists, and public agencies have been working together to develop high-accuracy, large-scale continu-

ously operating GPS reference stations that provide them the capability to monitor and model crustal deformation, tectonic plate movement, and the effects of geohazards such as earthquakes and volcanic eruptions.

Now, GNSS-augmented advance warning systems are going into place that can give us a crucial margin of safety in the event of an earthquake.

And none too soon.The latest Updated National Seismic Haz-

ard Maps recently released by the U.S. Geo-logical Survey (USGS) indicate a higher level of earthquake risk for the West Coast and some areas of the Midwest and East Coast then previously thought. (See the related news article in this issue on page 18.) In the next 30 years, the USGS says, California has a 99.7 percent chance of a magnitude 6.7 or larger earthquake, and the Pacific Northwest has a 10 percent chance of a magnitude 8 to 9 megathrust earthquake on the Cascadia subduction zone.

The Federal Emergency Management Agency (FEMA) has estimated the average annualized loss from earthquakes nationwide to be $5.3 billion. According to FEMA, 77 per-cent of that figure ($4.1 billion) comes from California, Washington, and Oregon, with 66 percent ($3.5 billion) from California alone.

So, an ongoing effort by the USGS and partner agencies and institutions to estab-lish a West Coast Earthquake Early Warning (WC-EEW) system as the prototype for an eventual nationwide “ShakeAlert” system seems especially timely.

The early warning system exploits physi-cal characteristics of earthquakes, which generate two main types of waves: rapidly moving primary or P-waves and the slower secondary (S) and surface waves that cause more intense and damaging ground shaking. (See accompanying figure.)

By detecting and analyzing the location and magnitude of an earthquake reflected in the P-wave energy, expected ground-shak-ing levels across a region can be estimated and warnings sent to local populations be-fore more damaging shaking arrives with or after the S-wave. The advanced warning can

range from seconds up to more than a min-ute, depending on the distance an affected area is from the earthquake’s origin.

Ken Hudnut, a geophysicist at the USGS Earthquake Science Center in Pasadena, California, and chair of the GNSS Working Group for the WC-EEW, has a long history in working in the area of geohazards. Dr. Hudnut received an A.B. degree in Earth sciences from Dartmouth College and a Ph.D. in geology from Columbia University. Before joining the USGS in 1992, he was a post-doctoral fellow at the California Institute of Technology Seismo-logical Laboratory and currently is a visiting associate in geophysics on the faculty of the California Institute of Technology.

We called on Dr. Hudnut to discuss the state of the art in seismic science and the role of GNSS in that research and in the design and op-eration of earthquake early warning systems.

The USGS has a long history of developing instrumentation for the study of earthquakes and other types of Earth movement. What does GNSS positioning bring to the task that seismic sensors do not provide and, more specifically, how do GPS/GNSS data benefit EEW systems?HUDNUT: GNSS positioning is especially good at rapidly giving us the change in a station’s position. Seismic sensors measure vibrations very well, but GNSS is better at measuring permanent displacement.

In a big earthquake, a station might move by several meters in several seconds, and not just in a simple straight line. The shaking may include erratic oscillatory displace-ments that are several times larger than the permanent displacement. Even though GNSS was never intended to measure such large, sudden, and jerky movements, we find that it works very well and provides a great augmentation to the seismic sensors that are currently in use for earthquake early warning.

How is GNSS data different from that obtained from these seismic sensors and how is it merged in an EEW system?HUDNUT: GNSS data add to system robust-ness because they are an independent measurement. The seismic sensors are basically a mass on a spring, whereas GNSS is measuring position variation using changes in ranges to a constellation of satellites, so it’s a totally different kind of observation. Having both types of data makes the system stronger because we can immediately rule out glitches coming from one sensor type or the other. The diversity of observations gives us more strength.

As for merging the data, there is an

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abundance of literature and we are test-ing everything from uncoupled to loosely coupled and tightly coupled, and using a variety of methods. There are trade-offs in terms of simplicity, speed, and smoothing that we’re evaluating on an ongoing basis. We are creating a hair-triggered system that is also very robust even during large dynamic displacements, which is not a slam dunk. We’ve learned a lot by studying the methods of combining being done for strap-down avionics, that is, navigation and positioning systems, and looking at both commercial off-the-shelf solutions and open-source options.

What GPS/GNSS signals, satellite observables, and signal components (e.g., code vs carrier phase) are used in the EEW system?HUDNUT: Right now, we are mostly reliant on GPS alone, but we have upgraded to GNSS receivers at our stations over the past several years. Of course we’re doing phase-differ-ential, dual-frequency processing to get the few-centimeter accuracy in real-time; so, we do widelaning and narrowlaning, but code is relatively unimportant to us — we rely heavily on the carrier phase. We’re using precise point positioning with ambiguity resolution, which is possible for GPS these days and in the future may be possible for GLONASS as well. Our limited telemetry bandwidth doesn’t allow us to bring back all of the GLONASS and other GNSS data just yet.

What practical benefits are provided by an impending seismic movement alert on the order of tens of seconds?HUDNUT: Applications envisioned are getting school children to safety under their desks that much sooner, and operating automatic shut-off valves, putting computer systems into a safer state, or switching other auto-

mated systems to try to prevent loss of life or damage to property. If you were having surgery performed at that time, wouldn’t you want the surgeon to remove the scalpel to safety right before the shaking started?

We want to make it possible for people to invent their own applications, and we expect this to happen here as it has in Japan, Mexi-co, and other countries that have already had EEW for many years and even decades. In Japan, EEW protects the Shinkansen (bullet train) system. In California, BART is testing use of EEW and figures it could help prevent or lessen the severity of future derailments.

In recent years, a number of demonstration campaigns have been conducted, involving public agencies and citizen participants in sending and receiving test notifications of an earthquake. What have been some the most important lessons learned from those campaigns?HUDNUT: ShakeOut is our annual public drill to encourage “Drop, Cover, and Hold On” by everybody. We started this in 2008 in California and it has grown worldwide. We use that as an earthquake hazard awareness opportunity for publicity for EEW. In general, ShakeOut encourages a personal action that could be done even quicker if one had an operational public EEW.

With the ShakeAlert EEW system, what we have been doing for the past couple of years is a slow roll-out through selected “beta-users.” We don’t want to roll this out to the public before it’s ready because of the “cry wolf” gotcha. Most county-level and large cities’ emergency operation centers, plus Caltrans and BART for example, have the ShakeAlert UserDisplay installed so that they could potentially relay an alert through dispatch communications systems.

Ken HudnutU.S. Geological Survey

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short- or long-term. Such errors would include biases result-ing from unmodeled atmospheric effects, satellite orbital errors, and so forth.

Measurement blunders would also be considered system-atic errors. In fact, equation (7) is used when assessing the reliability and integrity of a positioning system in the pres-ence of blunders.

Random errors such as multipath and noise, however, are usually treated a bit differently. Specifically, these errors are usually well characterized by their standard deviation only (i.e., no bias), meaning their effect can be completely reflected in the measurement covariance matrix.

If this is the case, the effect of these errors on the solution is directly obtained from the covariance matrix of the esti-mated parameters, which is computed as

This is a by-product of the law of propagation of variances. As before the result is affected by the measurement geometry and the measurement accuracy.

SummaryThis article looked at how measurement errors propagate into positioning errors. The primary factors affecting this propa-gation are measurement geometry and the measurement accuracy. This explains the motivation for receivers that minimize measurement errors (especially multipath) and that track as many satellites as possible.

GNSS SOLUTIONS


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In the past 20 years GPS has simul-taneously revolutionized both our modern infrastructure (by provid-

ing real-time navigation, mapping, and timing support) and our geodetic/surveying capabilities (by providing millimeter/centimeter-level position-ing). At this point, most of the GNSS innovations we expect to see in the next decade will come from calculating positions more accurately and faster, while expanding from GPS to use of all available GNSS signals.

Twenty years ago, in an article in ESA Journal (see Additional Resources section near the end of this article) Manual Martin-Neira presented a new application for GNSS. Instead of processing the direct GNSS signals for positioning, timing, and atmospheric

studies, Martin-Neira suggested employing reflected GNSS signals as the measurement. The first GNSS reflection experiments were focused on altimetry, ocean winds, and soil mois-ture; later researchers evaluated GNSS reflectometry for sensing snow/ice and measuring vegetation growth.

Each of these reflection studies used GNSS instruments specially designed to measure reflected signals. In contrast, geodesists and surveyors use GNSS instruments that we know are designed to suppress reflected signals (more commonly referred to as multipath). While these reflections are known to affect the accuracy of positions derived from these instruments, there is still no standardized approach that models (and eliminates) the effect of reflections.

Increasingly sophisticated uses of GNSS observables have led to a new era in remote sensing. A team of researchers describe the results of the applications of interferometric reflectometry to measure snow depth, vegetation water content, and soil moisture.

Environmental SensingA Revolution in GNSS Applications

KRISTINE M. LARSON DEPARTMENT OF AEROSPACE ENGINEERING SCIENCES, UNIVERSITY OF COLORADO

ERIC E. SMALLGEOLOGICAL SCIENCES, UNIV. COLORADO

JOHN J. BRAUNCOSMIC, UNIVERSITY CORPORATION FOR ATMOSPHERIC RESEARCH

VALERY U. ZAVOROTNYEARTH SYSTEM RESEARCH LAB/NOAA


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In principle, this would suggest that GNSS reflection research is irrelevant for the tens of thousands of geodetic-quality GNSS receivers currently in operation around the world. Certainly GNSS reflections were never consid-ered as a potential source of soil mois-

ture, snow depth, and vegetation data when the EarthScope Plate Boundary Observatory (PBO) <http://pbo.earth-scope.org> was built in the western United States between 2005 and 2008.

Unexpectedly, we have shown that these environmental data can be

retrieved from PBO data without any instrumentation beyond the existing GNSS data stream. We can do this because the multipath data turn the GNSS site into a quasi-interferometer. The distance between the antenna and the surface reflecting material derived from the interferometric effect will tell us whether the top of the surface has moved. This means we can use the data to measure snow depth by comparing it to data when there is no snow.

If the reflected signal travels through vegetation, the interferometer will show two effects: the primary reflection is caused by the top of the soil layer and secondarily, the ampli-tude of the reflected power will be smaller because it interacted with water in the vegetation. Changes in soil mois-ture cause the smallest changes to the interferometric effect. We can think of these as being caused by the signals being reflected by the surface soil layers having various wetness levels.

These new measurements of soil moisture, snow, and vegetation measure-ments (called the PBO H2O network) are needed both for climate studies and satellite validation. Water managers use the data to predict, and hopefully mitigate, hazards such as floods and droughts. These new GNSS environmen-tal data fill a niche between existing sat-ellite sensors (that have very large foot-prints) and other in situ sensors (which tend to have very small footprints).

This article describes how we have created an operational GNSS environ-mental sensing network. We will first describe the network itself, followed by an overview of how reflections manifest themselves in GNSS observations, and ending with examples of environmental signals we have measured using this network in the western United States.

The Plate Boundary Observatory and ReflectionsConsisting of about 1,100 stations, PBO was built by UNAVCO <http://www.unavco.org> under a contract with the U.S. National Science Foundation with the scientific goal of studying

Most PBO sites are operated with banks of batteries that are powered by solar panels, as shown at PBO site P422. Inset: PBO site P101 in Randolph, Utah, which is used to measure snow depth in the winter and vegetation/soil moisture in the spring, summer, and fall. UNAVCO photos


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the motion of tectonic plates and the deformation of the North American continent. The locations of PBO sites (Figure 1) were chosen to address spe-cific geophysical problems; thus, half of the GNSS sites are near fault zones in California. The east-west trending sites in Nevada and Utah are measuring motion in the Basin and Range prov-ince. Clusters of sites are also located near volcanoes (e.g. Yellowstone, Mammoth, Mount St. Helens, and the Aleutian arc).

After site locations were selected, the PBO project made special efforts to attach the GPS antenna to bedrock. Their reason for doing so was to ensure that the position (and velocity) infor-mation measured at each site would represent motion related to faults and

volcanoes. For this reason, almost no PBO sites are located on buildings.

Figure 2 presents a schematic of a typical PBO site. A dual-frequency, choke-ring antenna is protected in an acrylic radome with a nearby equip-ment box housing the receiver and the telemetry hardware. The antenna’s “drill-braced monument” is anchored to a depth of nearly three meters.

The standard PBO site operates a dual-frequency carrier phase receiver collecting GNSS signal data with a 15-second sampling interval; most also support 1-second sampling. With the exception of some sites in Alaska, the data are telemetered to the central UNAVCO facility in Boulder, Colo-rado, after midnight UTC each day; files of carrier phase and pseudorange

data (called RINEX files) are produced by UNAVCO and posted online for public access soon after. Geophysicists are able to download the RINEX files for reprocessing, or they can download the frequently updated position time series in a standard terrestrial reference frame.

Velocity products that are used to study faults, earthquakes, and volcanoes, are also produced for the geophysical community. These PBO positioning products are based on very detailed models of the GNSS space-craft, propagation delays, and Earth motions.

Although geophysicists and geodesists are well aware of the negative effects of reflected signals, there is still no standard model to remove reflection/multipath from these position/velocity products. This is partly because each GNSS site has unique reflection characteristics. Furthermore, many efforts to model multipath rely on stacking carrier phase residuals from least squares analyses. In principle these residuals could be used for environmental sensing; however, they can and will be influenced by mismodeled carrier phase data. Consequently, parameters in the least squares analysis could thus absorb or mask what was a real environmental change.

On the other hand, if one thinks about how best to measure multipath reflections rather than trying to model multipath corrections for carrier phase data, one might recast the problem to use signal power data. These are the analogous data to what is being used by the GNSS reflectometry commu-nity, which typically uses two receiv-ers/antennas to separately measure the direct and reflected signal. The GNSS units used by geodesists and survey-ors produces a single data stream and measurements that represent the interference of the direct and reflected signal. In the latter case, the antenna is not tuned to measure the reflected signal as it is with traditional GNSS reflectometry.

So, a key question arises: Are the signal power data collected by geo-

ENVIRONMENTAL SENSING

FIGURE 1 Circles represent locations of Plate Boundary Observatory (PBO) GNSS sites. Blue and cyan colored circles represent locations for PBO H2O product release versions 1 and 2.

FIGURE 2 Schematic illustration of a typical PBO site. The radome protects the choke-ring antenna. Most sites are powered by solar panels. Except for some sites in Alaska, the data are telemetered at least daily. Credit: UNAVCO.


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detic/surveying GNSS units of sufficient quality to become inadvertent environmental reflectometers?

GNSS receivers generate carrier-to-noise density ratio data, which are stored in a RINEX file as signal-to-noise ratio (SNR) observables. Figure 3 shows representative SNR data set collected by a geodetic-quality GNSS receiver. The direct signal has a simple polynomial shape, with lower SNR magnitudes at the rising and setting sections of the satellite track. These lower values primarily result from the antenna gain pattern. Superimposed on the direct signal are the reflected signals, which for horizontal planar reflectors manifest themselves as oscillations. Note particularly that little evidence of reflected signals appears above elevation angles of around 25 degrees. This is again due to the antenna gain pattern.

The transmitted GPS signal is right hand circularly polarized (RHCP). The reflection will have both RHCP and LHCP (left hand circularly polarized) components. As seen in Figure 4, the reflection coefficients are different for RHCP and LHCP, and depend on both the reflection surface and the satellite elevation angle.

The frequency of the reflected SNR signal is dominated by geometry, i.e., the extra path length traveled by the reflected signal, as seen in Figure 5. For a planar horizontal reflector, the frequency of the interference of the direct and reflected signal observed in SNR data is constant as a function of sine of the elevation angle. It is straightforward to extract this dominant frequency using a periodogram or estimate of the spectral density of the signal, a quantity that we call the effec-tive reflector height.

If the effective reflector height changes, this means that the surface layer around the antenna changed. For example, an effective reflector height would change from 2.0 to 1.8 meters if it snowed 0.2 meters. To convert these effective reflector heights into an absolute measure of snow depth, we compare effective reflector heights estimated during the win-ter months with effective reflector heights determined when no snow is on the ground.

The amplitude of the reflection observed in the SNR data depends on the dielectric constant of the surface material — and, thus, very wet snow produces a different amplitude than very dry snow. Likewise, vegetation with high water content

has much smaller SNR amplitudes than vegetation with very low water component. This is the principle used to define the vegetation statistic reported by PBO H2O.

In order to define the snow depth, soil moisture, and veg-etation water content measurements more rigorously we have developed forward models that contain information about the transmitted GPS signal, the gain pattern for the antenna used by PBO, and reflection coefficients for natural surfaces. These models have guided us in developing retrieval algo-rithms, which have been automated for PBO H2O and pub-lished in the refereed literature. As part of this effort we have

FIGURE 3 SNR data from the L2C signal collected at PBO site P041 for a single satellite track are shown in black. The smooth blue curve represents the direct signal. Elevation angles are shown in gray. The oscillations at low elevation angles are indicative of reflected signals/multipath effects.

FIGURE 5 Schematic of multipath geometry for a horizontal planar surface. The direct L2 signal (shown in blue) is reflected at a planar surface and travels an additional distance (shown in red). Elevation angle is depicted by e. The GNSS unit measures the interference between the direct and reflected signals (examples of this interference are shown in the inset).

FIGURE 4 Reflection coefficients for a variety of natural surfaces at GPS frequencies and RHCP (dashed) and LHCP (solid) signals



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also conducted validation experiments where we measured soil moisture, snow depth, and vegetation water content in situ. These experiments have been invaluable in allowing us to improve our algorithms.

Results from PBO H2OThe PBO H2O initiative grew out of experiments conducted near Boulder, Colorado between 2007-2009. After several years of developing models and retrieval algorithms, the PBO H2O network began operating in October 2012 with a data portal providing online access to users <http://xenon.colora-do.edu/portal>. Figure 1 provides the location of the approxi-mately 350 current sites along with about 200 new sites for which we plan to begin distributing data in the fall of 2014.

Data are downloaded from the central UNAVCO archive every evening, and new solutions for soil moisture, snow depth, and vegetation water content are posted each morn-ing. To aid in quality control for our products, we also down-load other environmental datasets, such as hourly samples of modeled precipitation and temperature data from the North American Land Data Assimilation System and snow cover data from NASA’s satellite-based Moderate Resolution Imag-ing Spectroradiometer (MODIS) project. These are useful for identifying outliers in our vegetation and soil moisture prod-ucts. Photographs, Google maps, digital elevation maps, and climatology information are also provided for each site.

The following sections describe a few examples from each environmental dataset.

Snow. Our first snow depth measurements were made in 2009 at a flat mesa site south of Boulder. Although the snow depth retrievals were successful, we needed to demonstrate that the technique would work in more challenging environ-ments. Figure 6 shows the next snow site we tested. We chose a Niwot Ridge, Colorado, site because of its topographic vari-ability (due to its location in a saddle at an elevation of about 3,500 meters), extreme cold, and very high winds. Power and Internet access was available from an existing scientific installation.

Five years later, the GPS snow depth time series from this site shows that the reflection method is robust, with very few data outages. Comparisons with in situ data (the pole in the photograph is measured roughly every two weeks) show that the method is also very accurate. Although the monument is three meters tall, as seen in the inset photograph in Figure 6, the antenna was almost buried in spring 2011. The latter was a banner snow year throughout the western United States, and a handful of PBO antennas were buried at snow peak.

Figure 7 shows snow levels measured at a PBO H2O site near Island Park, Idaho. Unlike the station position time series generated for this site by geophysicists, which shows almost no variability, the snow changes at the site are quite


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dynamic. The first snowfall generally occurs at the same time each fall, but the peak snow amount is highly variable, as is the timing of snowmelt. The latter measurement is particu-larly important for predicting potential flooding. A video combining a series of photos of the station with correspond-ing weekly snow level plots may be viewed online at <http://


FIGURE 7 Five years of snow depth time series for the GPS site at P360 in southern Idaho.

FIGURE 6 Top: five years of snow depth time series for the GPS site at Niwot Ridge, Colorado; bottom: Niwot Ridge GPS installation photo-graphed in fall and early spring.


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Vegetation. PBO H2O vegetation measurements (NMRI, Normalized Microwave Reflection Index) are based on changes in the reflection amplitude, where values of zero represent the vegetation with the lowest water content. Fig-ure 8 shows the vegetation data for a GNSS site in eastern Wyoming designated P042. This figure compares the NMRI vegetation water content estimates derived from GPS satel-lite data with the site’s normalized difference vegetation index (NDVI). The latter are optical measurements — typi-cally generated at 16-day intervals — using MODIS sen-sors that measure greenness, with each pixel representing a 250-square-meter footprint.

Greenness correlates strongly with photosynthesis pro-duction, and thus NDVI is commonly used to study vegeta-tion growth. To provide some context, Figure 8 also shows modeled precipitation data (which is not directly measured at PBO sites). A close correlation appears between the GPS NMRI data and NDVI (correlation coefficient of 0.86). Par-ticularly note the absence of greenness in 2012 and low GPS

FIGURE 8 Top: GPS site at Wheatland, Wyoming; middle: GPS vegetation measurements (blue) compared with Normalized Difference Vegetation Index (green); bottom: cumulative precipitation from the North Ameri-can Land Data Assimilation System (NLDAS). The GPS vegetation index is also called the Normalized Microwave Reflection Index (NMRI).


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values during the 2012 drought. That year had low snowfall, a very hot spring, and very little rain. The P042 data record also shows a double growth peak in 2008. This phenomenon occurs when there are large gaps between rainfalls.

Similar sensitivity to vegetation growth is shown for California site P532 (Figure 9). Here both the GPS and NDVI records show strong sensitivity to the effects of drought, including 2014. Note that, although the rains in late February 2014 brought cumulative precipitation levels up to near the five-year average, vegetation water content as measured by NMRI has not recovered. Droughts effects in 2007 and 2009 are also clearly visible. A further note: the GPS vegeta-tion data have a shorter “season length” than the NDVI data, with NDVI having a consistently longer growth season than the GPS measurement of vegetation water content.

Because GNSS reflections are sensitive to vegetation water content and NDVI is correlated to chlorophyll production, the combination of these measurements pro-

vides better constraints to phenologists studying the influ-ence of climatic variations on periodic plant life cycles.

Soil Moisture. Soil moisture is the most challenging water cycle parameter to measure with GNSS receivers and faces some limitations. First, the reflection technique cannot mea-sure soil moisture if there is snow on top of the soil. For PBO H2O sites in the Rocky Mountains, we must remove data affected by snow. Second, soil covered by vegetation with very high water content (such as alfalfa) requires a more complex model of the reflections than we currently use.

Even with these restrictions, we have found many PBO sites that generate accurate soil moisture records. Figure 10shows such a record from a GNSS site near San Jose, Cali-fornia. Note that there is strong correlation between soil moisture changes and precipitation events, and then there is a “dry down.” This is consistent with the behavior of a shal-low (0-5 centimeter) soil moisture instrument. The sensing depth of the GNSS method is determined by its transmission frequency (L-band).

Although measurements of soil moisture are needed at depth as well as the surface, these GNSS data are particu-larly useful for satellite validation (ESA’s SMOS mission and NASA’s upcoming SMAP launch) because these sensors also operate at L-band.

Expanding PBO H2O to International GNSS NetworksAlthough the initial emphasis of our project was to use data from the PBO network, we must stress that no technical reason exists which prevents GNSS instruments operated by surveyors and transportation agencies from being used for environmental sensing. Both geophysicists and surveyors use dual-frequency carrier phase GNSS receivers — and, if prop-erly configured, such receivers can generate SNR data that are


FIGURE 9 GPS vegetation growth index (NMRI) compared with NDVI and accumulated NLDAS precipitation at PBO site P532 located 50 miles northwest of Santa Barbara, California.

FIGURE 11 Top: GNSS site operated by the Mesa County Surveying Network located in Snowmass, Colorado; time series of snow depth for COA1 is shown for the 2014 water year; Bottom: the GNSS station operated near Ashland (ASHL) by the Minnesota Department of Transportation is located on the side of a road. The GNSS satellite tracks from the west of the monument can be used to measure snow depth.

FIGURE 10 Daily measurements of volumetric soil moisture measured with GNSS (blue) and daily precipitation from NLDAS.


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suitable for reflectometry applications. Configuration examples include

requesting that the receiver track both legacy (L2 P-code) and new civilian (L2C) signals. The latter is preferred for reflection research because the code is public and the extracted signal power is higher. Second, some receivers pro-duce SNR data rounded to the closest integer by default, which the station operator can easily change so that it generates a more precise SNR data stream.

To demonstrate that surveyor-oper-ated GNSS sites can be used as snow sensors, we have recently partnered with two surveying organizations to expand PBO H2O. Figure 11 shows two examples from these efforts. In Colo-rado we have accessed data from the Mesa County Real-Time Virtual Refer-ence Network <http://emap.mesacou-nty.us/GPS_Survey/GPS_Survey.htm>; the Minnesota data are distributed by the State Department of Transporta-tion. Because the Minnesota sites tend to be located near highways, we used Google Earth images to window the data we used to measure show depth. For the Colorado sites, we used both photographs and Google Images. In both cases accuracy of the snow depth estimates is equivalent to that recov-ered from the PBO sites.

Can we measure soil moisture, snow depth, and vegetation at all GNSS sites? Unfortunately, the short answer is “no.” Many GNSS sites have been installed on buildings and/or near parking lots, where reflections would be of little interest for the purposes described in this article. The locations of these sites also produce degraded positioning accuracy, but the degrada-tion is often acceptable to the primary users of the data — geodesists, survey-ors, and others.

The second limitation to using GNSS networks for environmental sensing has to do with data availability. While many organizations provide a RINEX file to national archives such as CORS, often these RINEX files do not include the SNR data. Furthermore, some archives degrade the RINEX files by eliminating observables and deci-

mating the remaining data. This makes it difficult — and in some cases impos-sible — to extract useful environmen-tal data from these. Although these issues constrain the use of data from some existing GNSS sites, we hope that results from PBO H2O encourages future installations in locations that can measure positions and environ-mental changes simultaneously.

Final RemarksGeodesists, geophysicists, and survey-ors have all established large GNSS networks. Nearly all of them have open data policies and encourage broad usage of their data. The vast majority of GNSS data users focus on positioning, although the timing and atmospheric communities also value data from GNSS networks. Here we have shown how to further extend the value of


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ground GNSS networks by describing how to routinely measure soil moisture, snow depth, and vegetation growth. These data are valuable both to scien-tists and water managers and a cost-effective use of existing infrastructure.

AcknowledgmentsThis work has been a collaboration that includes many colleagues, stu-dents and post-docs: Felipe Nievinski, Ethan Gutmann, Andria Bilich, Karen Boniface, James McCreight, Cheney Shreve, Clara Chew, Sarah Evans, Evan Pugh, John Pratt, Penina Axelrad, and Praveen Vikram. The PBO H2O portal is supported by NSF EAR-1144221 and NASA NNX12AK21G. PBO is oper-ated by UNAVCO for EarthScope, and supported by NSF (EAR-0350028 and EAR-0732947).

ManufacturersThe standard PBO sites use NetRS GNSS receivers from Trimble, Sunny-vale, California, USA.

Additional ResourcesGNSS-Reflectometry[1] Cardellach, E., and F. Fabra, A. Rius, S. Pet-tinato, and S. D’Addio, “Characterization of dry-snow sub-structure using GNSS reflected signals,” Remote Sensing of Environment, Vol. 124, 122-134, 2012[2] Egido A., and M. Caparrini, R. Ruffini, S. Paloscia, E. Santi, L. Guerriero, N. Pierdicca, and N. Floury, “Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture,” Remote Sensing, Vol. 4(8), 2356-2372, doi:10.3390/rs4082356, 2012.[3] Garrison, J. L., and S.J. Katzberg, “The Application of Reflected GPS Signals to Ocean Remote Sensing,” Remote Sensing of Environ-ment, Vol. 73(2), 175-187, doi:10.1016/s0034-4257(00)00092-4, 2000[4] Garrison J. L., and A. Komjathy, V.U. Zavorot-ny, and S.J. Katzberg, “Wind speed measurement using forward scattered GPS signals, IEEE Trans-actions on Geoscience and Remote Sensing, Vol. 40(1): 50–65, 2002[5] Gleason S, S. Hodgart S. Yiping, C. Gom-menginger, S. Mackin, M. Adjrad, and M. Unwin Detection and Processing of bistati-cally reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing, IEEE Transactions on Geoscience and Remote Sensing, Vol. 43(6),1229-1241. doi:10.1109/TGRS.2005.845643, 2005[6] Katzberg S.J., O. Torres, M.S. Grant, and D. Masters, Utilizing calibrated GPS reflected

signals to estimate soil reflectivity and dielec-tric constant: Results from SMEX02. Remote Sensing of Environment, Vol. 100(1), 17-28. doi:10.1016/j.rse.2005.09.015, 2005[7] Martin-Neira M., “A Passive Reflectometry and Interferometry System (PARIS)-Application to Ocean Altimetry,” ESA Journal, Vol. 17(4), 331-355, 1993.[8] Ruf, C., and A. Lyons, M. Unwin, J. Dickinson, R. Rose, D. Rose, and M. Vincent, “CYGNSS: Enabling the Future of Hurricane Predic-tion,” IEEE Geoscience and Remote Sensing Magazine, Vol. 1(2), 52-67, doi: 10.1109/MGRS.2013.2260911, 2013[9] Semmling, A. M., and T. Schmidt, J. Wickert, S. Schon, F. Fabra, E. Cardellach, and A. Rius, “On the retrieval of the specular reflection in GNSS observations for ocean altimetry,” Radio Sci-ence, Vol 47, doi:10.1029/2012RS005007, 2012[10] Yang, D., and Y. Zhou and Y. Wang, “Remote Sensing with Reflected Signals: GNSS-R Data processing Software and Test analysis,” Inside GNSS, September/October 2009, pp. 41–45

GNSS-Interferometric Reflectometry[1] Chew, C. C., and E. E. Small, K. M. Larson, and V. Zavorotny, “Effects of Near-Surface Soil Mois-ture on GPS SNR Data: Development of a Retriev-al Algorithm for Volumetric Soil Moisture,”” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52(1), 537-543, doi:10.1109/TGRS.2013.2242332, 2014[2] Larson, K. M., E. E. Small, E. Gutmann, A. Bilich, J. Braun, and V. Zavorotny, “Use of GPS receivers as a soil moisture network for water cycle studies,” Geophysical Research Letters, Vol. 35, L24405, doi:10.1029/2008GL036013, 2008[3] Larson, K. M., E. Gutmann, V. Zavorotny, J. Braun, M. Williams, and F. Nievinski, “Can We Measure Snow Depth with GPS Receivers? Geophysical Research Letters, Vol. 36, L17502, doi:10.1029/2009GL039430, 2009[4] Larson, K. M., and F.G. Nievinski, “GPS Snow Sensing: Results from the EarthScope Plate Boundary Observatory,” GPS Solutions, Vol 17(1), 41-52, doi 10.1007/s10291-012-0259-7, 2013[5] Nievinski, F. G., and K.M. Larson, “Forward modeling of GPS multipath for near-surface reflectometry and positioning applications,” GPS Solutions, Vol. 18(2), 309-322, doi:10.1007/s10291-013-0331-y, 2014[6] Ozeki, M., and K. Heki, “GPS snow depth meter with geometry-free linear combinations of carrier phases,” Journal of Geodesy, Vol. 86(3), 209–219, doi:10.1007/s00190-011-0511-x, 2012[7] Small, E. E., and K. M. Larson and J. J. Braun, “Sensing Vegetation Growth with GPS Reflec-tions,” Geophysical Research Letters, Vol. 37, L12401, doi:10.1029/2010GL042951, 2010[8] Zavorotny, V., and K. M. Larson, J. J. Braun,

E. E. Small, E. Gutmann, and A. Bilich, “A physical model for GPS multipath caused by ground reflections: toward bare soil moisture retrievals,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sens-ing” (JSTARS), Vol. 3(1), pp. 100-110, 10.1109/JSTARS.2009.2033608, 2010

AuthorsKristine M. Larson received the B.A. degree in engineering sci-ences from Harvard University and the Ph.D. degree in geo-physics from the Scripps Insti-

tution of Oceanography, University of California at San Diego. She was a member of the technical staff at the Jet Propulsion Lab from 1988–1990. Since 1990, she has been a professor in the Department of Aerospace Engineering Sciences, University of Colorado, Boulder. Her research interests are focused on developing new appli-cations and techniques for GPS.

Eric E. Small received a B.A. degree in geological sciences from Williams College and the Ph.D. degree in earth sciences from the University of California

at Santa Cruz. He is a professor in the Depart-ment of Geological Sciences, University of Colo-rado, Boulder. His research is focused on land surface hydrology.

John J. Braun received the B.A. degree in physics and mathe-matics from the University of Colorado, Boulder, and the Ph.D. degree from the Depart-

ment of Aerospace Engineering Sciences, Uni-versity of Colorado. He is a project scientist within the COSMIC program at the University Corporation for Atmospheric Research, Boulder. His research interests include developing new techniques and using GNSS observations to study the Earth and its environment, particularly the water cycle.

Valery U. Zavorotny received the M.Sc. degree in radio physics from Gorky State University, Gorky, Russia, and the Ph.D. degree in physics and mathe-

matics from the Institute of Atmospheric Phys-ics, USSR Academy of Sciences, Moscow. From 1971 to 1990, he was a research scientist with the Institute of Atmospheric Physics of the USSR Academy of Sciences, Moscow. From 1991–2000, he was a CIRES Research Associate in the Envi-ronmental Technology Laboratory of the National Oceanic and Atmospheric Administration (NOAA), Boulder, CO, and became a NOAA/ETL physicist in 2000. His research interests include theory of wave propagation through random media, wave scattering from rough surfaces, and ocean and land remote sensing applications.


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The small satellite “Technologie-Erprobungs-Träger 1” (TET-1) is the first spacecraft developed for

the German Aerospace Center (DLR) On-Orbit-Verification (OOV) program, which provides f light opportunities dedicated to testing and qualification of new technologies in space. The satellite was lifted into a low-Earth orbit (LEO) on July 22, 2012, from the launch site in Baikonur, Kazakhstan.

TET-1 carries various technology demonstration payloads, among them the Navigation and Occultation eXperi-ment (NOX). This payload consists of a geodetic-grade GPS receiver, which is connected via an antenna selector to two GPS L1/L2 patch antennas.

One of the antennas is mounted on the satellite’s zenith panel and receives signals primarily used for precise orbit determination (POD) experiments. The second antenna is pointed towards the anti-flight direction of the satellite for

collecting measurements of low eleva-tion satellites for ionospheric and tro-pospheric occultations. The antenna switch allows to select either the POD or the occultation antenna for signal reception.

This article describes the NOX pay-load on board the TET satellite in detail and analyzes the receiver’s tracking per-formance and the accuracy of its naviga-tion solution. It also presents the initial tracking results of the occultation anten-na, which demonstrate that GPS signals can be tracked through the ionosphere below the satellite’s local horizon — at a minimum, even down to the upper part of the atmosphere — with commercial-off-the-shelf (COTS) equipment.

Spacecraft Design & OperationThe spacecraft bus for TET is based to a large extent on the Bi-Spectral Infra-Red Detection (BIRD) satellite bus. The satel-

A test and evaluation program demonstrates that a commercial GPS receiver can operate as a spaceborne research tool.

The Navigation and Occultation eXperiment

GPS Receiver Performance On Board a LEO Satellite

ANDRÉ HAUSCHILD, MARKUS MARKGRAF, OLIVER MONTENBRUCKGERMAN SPACE OPERATIONS CENTER (GSOC) GERMAN AEROSPACE CENTER (DLR), GERMANY


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lite has a height of 880 millimeters and a depth of 670 millimeters. In launch configuration, TET-1’s width is 580 mil-limeters, which increases to 1,540 mil-limeters with the solar panels deployed. Its total mass is approximately 120 kilo-grams, which includes 50 kilograms of payloads.

TET-1 is equipped with star sensors, sun sensors, gyroscopes, and magnetic field sensors for attitude determination. A set of four reaction wheels and mag-netic coils provides three-axis stabiliza-tion. The satellite bus system is equipped with a single-frequency receiver with its own dedicated patch antennas. Note that this system, which provides position-ing and timing for the satellite bus, is completely independent from the NOX payload. The satellite carries 11 payloads in total, including the NOX, which has been designed to demonstrate the suit-ability of COTS technology for space applications.

Figure 1 shows a schematic illus-tration depicting the front view of the TET-1 satellite without its multi-layer insulation. The figure shows the various components of NOX, which will be dis-cussed in further detail in the following section, and illustrates the orientation of the body-fixed coordinate system of the satellite.

The TET-1 satellite is operated in dif-ferent attitude modes depending on the payload operation or mission require-ments. The two attitude modes relevant for the operation of the NOX system are the Earth-pointing mode (EPM) and the Sun-pointing mode (SPM). In the EPM, the satellite’s body-fixed “+x”-axis points into the direction of flight and the “+z”-axis is oriented towards the center of the Earth. The SPM is used to recharge the satellite batteries. For this purpose, the satellite’s solar panels, which are mount-ed on the “-z”-panel are pointed towards the Sun to maximize their power output.

TET-1 was launched into a sun-synchronous LEO orbit at a height of approximately 500 kilometers with an inclination of 97.5 degrees on July 22, 2012. After testing all satellite subsystems and payloads during the commissioning phase after launch, the OOV mission was conducted until October 2013. The satel-lite still continues operation and is now part of the Firebird mission, with the task of fire detection from orbit.

Overview of the NOXThe Navigation and Occultation eXperi-ment on TET-1 has been designed to demonstrate the suitability of commer-cial-off–the-shelf technology for space applications. Figure 2 provides a sche-matic of the NOX hardware layout. The experiment consists of a dual-frequency GPS receiver connected to an RF relay, which allows operators to select one of two L1/L2 passive patch antennas for signal tracking. A low noise amplifier

On July 22, 2012, the first small German satellite in the On-Orbit-Verification program was carried into orbit from the Cosmodrome in Baikonur, Kazakhstan, by a Russian Soyuz launch vehicle. TET-1 is a technology testbed with 11 experiments on board that have been operated in space for a year. DLR photo. Left: Artist’s impression of the TET-1 small satellite. DLR/Astro- und Feinwerktechnik Adlershof GmbH


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(LNA) with a gain of 26 decibels at the L1 and L2 frequencies is used to ensure an adequate signal strength at the receiver input.

The receiver is standard off-the-shelf hardware. Standard receiver firmware, however, cannot be used for spaceborne applications, because height- and velocity-constraints pro-hibit the operation on board a satellite. The NOX receiver is therefore equipped with a special firmware with these NATO limits removed. In addition to these modifications, the Dop-pler search window has been increased 45 kilohertz to facilitate signal acquisition under high dynamics.

With its 48 channels the receiver can track L1 C/A code and L1/L2 P(Y) code signals of 16 satellites simultaneously. It provides pseudorange, carrier-phase, Doppler, and carrier-to-noise density ratio (C/N0) observations for L1 C/A and L2 P(Y) signals and as well as pseudoranges and C/N0 for L1 P(Y) at data rates of up to 10 Hz.

The receiver underwent extensive pre-flight testing to ensure its suitability for use in space. The papers by J. Leyssens et alia and M. Garcia-Fernandez, listed in the Additional Resources section near the end of this article, describe this testing in fur-

ther detail. The receiver board is mounted together with an interface (IF) board on the “-y”-panel of the payload compart-ment of the satellite as shown in Figure 1. The interface board serves as the power and commanding interface between NOX and the TET satellite bus. It also contains a protection circuit against single-event latch-up effects and a switch line, which allows the operation of the RF relay for antenna selection.

The antennas of NOX are two identical patch antennas, which are mounted on different sides of the satellite bus struc-ture without dedicated ground planes or choke rings. The anten-na used for precise orbit determination is mounted on the “-z”-panel of the satellite. The POD antenna points towards zenith, when TET-1 is operated in Earth-pointing mode. This attitude is therefore preferred for the operation of the navigation system, because it maximizes the visibility of GPS satellites.

Restrictions of the battery capacity, however, require TET-1 satellite to be operated in Sun-pointing mode for recharging when it is not in the Earth’s shadow. As a result, the POD anten-na’s boresight vector does not always point towards zenith, but deviates significantly from the local zenith vector during parts of the orbit when the batteries are charged. As a result, the antenna’s field of view will be obstructed by the Earth, which limits the number of satellites available for tracking. We will show, however, that the NOX payload can still provide robust navigation solution most of the time.

The second antenna for radio occultation is mounted on the “-x”-panel. It points towards the Earth’s horizon in anti-flight directions when the satellite is in Earth-pointing attitude mode. This antenna orientation facilitates the tracking of GPS signals through the Earth’s atmosphere for GPS radio occultation (RO) measurements, which we will discuss in more detail later.

As only one antenna can be used at a time, the LEO orbit and clock offset determination during occultation experiments is performed using measurements of the occultation antenna. This is a simplified concept compared to most modern RO mis-sions, which use a dedicated navigation antenna in parallel to one or more occultation antennas.

We should mention that the RO experiment of NOX is not intended to routinely provide data for weather prediction or climate research. Its purpose is merely to demonstrate the capa-bilities and limitations of current COTS hardware, along the lines of a similar experiment conducted on board the Micro-Lab 1 in 1995 and described in the article by R. Ware et alia.

Receiver PerformanceIn the following subsections, we analyze the in-flight perfor-mance of the GPS receiver and the antenna system with respect to activation behavior and signal tracking characteristics. Where available, the on-orbit results are compared to pre-flight tests with a signal simulator.

Receiver Start-Up Behavior. Unlike the GPS receiver on the satellite bus, which is operated continuously during the entire mission, the NOX receiver is only activated during dedicated experiment time slots typically once a week. The experiments

ON BOARD A LEO SATELLITE

FIGURE 1 Drawing of the TET-1 satellite without multilayer insulation showing key elements of NOX and the orientation of the satellite’s body-fixed coordinate system. The white plate above the NOX housing is a heat radiator, which is part the of the satellite’s thermal control system (image courtesy Kayser-Threde/Astro- und Feinwerk-technik).

NOX POD antenna (not visible, on “-z”-panel)

NOX housing with GPSreceiver and IF board

NOX occultation antenna(on “-x”-panel)

+x

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FIGURE 2 Schematic of the NOX payload on-board TET [7]

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have a varying duration between 12 and 24 hours. The receiver is only supplied with power during these intervals and other-wise is completely switched off.

Upon activation at the beginning of an experiment’s time slot, the receiver starts to search for satellite signals. The receiv-er’s non-volatile memory still contains the last valid position solution, broadcast almanacs and navigation data obtained before the last deactivation. In the case of the NOX receiver, this information is used to compute a list of visible satellites, which are prioritized in the signal search. If this warm start acquisition fails because no satellite has been acquired after 45 seconds, a sequential search over the full GPS constellation is performed instead until a position fix could be successfully computed.

The receiver’s time to first fix (TTFF) is an important per-formance measure. The histogram in Figure 3 shows the TTFF statistics for 20 receiver activations using the POD antenna. These data indicate that the receiver has achieved a first posi-tion fix after less than two minutes in more than half of the cases. Note that this includes the time necessary for receiver boot and self-test.

The shortest and longest TTFF encountered in the 20 receiver activations are 85 seconds and 189 seconds, respec-tively. The mean and standard deviation of the TTFF is 2.03 ±

0.50 minutes, which show good agreement with values obtained from hardware-in-the-loop tests using a signal simulator. The TTFF for the simulated scenario yielded a mean and standard deviation of the TTFF of 2.56 ± 0.50 minutes, as described in the article by J. Leyssens et alia. The short TTFF clearly shows the benefits of a smart signal-search concept and optimal use of the 48 channels during acquisition.

FIGURE 3 Time to first fix of the internal receiver navigation solution after receiver activation. The diagram is based on 20 receiver starts using the POD antenna.

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It is interesting to note that the receiver has never acquired a position fix within the first 45 seconds of activation, meaning that the first fix has always been achieved after a signal search over the full constellation. Using almanac and navigation solu-tion information from previous fixes during a warm start is obviously not helpful due to the long interruptions between consecutive activations of the NOX payload.

Tracking Performance. The number of simultaneously tracked satellites is a key parameter to determine the avail-ability of a navigation solution. Figure 4 shows the statistics

for the number of satellites tracked on L1 C/A-code and L2 P(Y)-code signals over the entire period of time when NOX was activated using the POD antenna. Statistics for L1 P(Y)-code are not displayed, because they are virtually identical with L2 P(Y) results.

The figure shows clearly that the receiver tracks between 8 and 12 satellites most of the time, which provides sufficient redundancy for a robust computation of a navigation solution. These results are consistent with results from signal-simulator tests. The peak for P(Y)-code is shifted slightly towards a lower number of simultaneously tracked satellites compared to C/A-code, which reflects a lower sensitivity and deferred acquisition of the semi-codeless tracking of P(Y)-code.

The receiver tracks four or more satellites for 99.99 percent of the time on C/A-code and 99.73 percent of the time on P(Y)-code. We can thus conclude that the receiver has a high avail-ability of single- and dual-frequency navigation solutions.

In order to assess the measurement quality of the NOX receiver and antenna system, we analyzed the carrier-to-noise-density ratio (C/N0). Figure 5 depicts a polar plot of the carri-er-to-noise-density ratio (C/N0) for C/A-code measurements based on 24 hours of data recorded on August 30, 2012, using the POD antenna. The coordinate axes in Figure 5 are aligned with the local frame of the antenna, which exhibits a different orientation than the satellite’s body-fixed coordinate axes.

The measured C/N0 ranges from 30 dB-Hz near the hori-zon to approximately 50 dB-Hz at higher elevation angles. The C/N0 pattern is not rotationally symmetric, but exhibits a clear azimuthal dependency. This effect is especially pronounced on the left side of the diagram and results most likely from the mounting position of the antenna close to the edge of the panel. Without a choke-ring or a dedicated antenna ground plane, the non-uniform satellite structure affects the gain pattern of the antenna.

Accuracy of Navigation Solution. The NOX receiver computes a navigation solution based on dual-frequency pseudorange observations and outputs results at intervals of 30 seconds. The receiver’s internal position filter has been turned off in the NOX experiment; thus, the reported positions correspond to independent epoch-by-epoch navigation solutions.

The errors of the receiver navigation solution are assessed by a comparison with a precise reference trajectory from a reduced-dynamic orbit determination based on carrier-phase measurements. In the next section we will provide more details on how the reference trajectory has been obtained.

For our analysis, we selected navigation solution results from a period of almost 24 hours on August 30, 2012. Figure 6shows the errors in radial, tangential (in the direction of flight), and normal directions with respect to the orbital coordinate frame with the corresponding statistics listed in Table 1.

It becomes obvious that the errors of the radial component exhibit the largest scatter, which is an expected result, because the vertical component is always most affected by the largest dilution-of-precision in a single-point solution.

FIGURE 5 Polar plot of carrier-to-noise-density ratio for C/A code measured with the POD antenna.

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FIGURE 4 Simultaneously tracked satellites on the L1 C/A and the L2 P(Y) signal using the POD antenna.

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For the majority of the epochs, the radial errors fall between ± 5.0 meters, whereas the tangential and normal components are typically less than ± 2.5 meters. At one epoch, though, the radial error reaches 13.81 meters.

When interpreting these results, it is important to note that the reference solution refers to the satellite’s center of mass whereas the receiver’s navigation solution refers to the antenna phase center. The offset between these two points is on the order of 0.66 meter and is projected differently on the radial-, tan-gential-, and normal-coordinates, depending on the satellite attitude, and affects the error statistics.

Nevertheless, we can conclude that the receiver typically provides navigation solutions with meter-level accuracy. The satellite has alternately been operated in Earth-pointing mode during eclipse and otherwise in Sun-pointing mode. Despite the possible obstruction of the antennas field of view in Sun-pointing attitude mode, the receiver has provided continuous navigation solutions for the entire time due to its high number of tracking channels and fast acquisition of satellites.

POD PerformanceIn this section, we will present the results of precise orbit deter-mination of NOX. Here we will briefly introduce the orbit determination process. As no reference solution is available for the satellite’s orbit, overlap comparisons serve as a metric to assess accuracy of the POD results. A phase-center pattern for the NOX POD antenna computed from carrier-phase residuals is also presented.

Reduced Dynamic Orbit Determination. Precise orbit solutions have been computed using measurements from the satellite’s POD antenna at an update rate of 30 seconds. As a first a priori trajectory, a single point solution based only on pseudorange measurements is computed. This coarse trajectory is smoothed using a least-squares filter to fit the satellite positions to a dynamic orbit model. This smoothed orbit is then used as an apriori orbit for a reduced dynamic orbit determination, where pseudorange and carrier-phase measurements are processed in a least-squares filter with a dynamical orbit model.

The estimation parameter vector comprises the satellite position and velocity state at the reference epoch, a scaling factor for the accelerations due to solar radiation pressure and atmospheric drag, as well as the ionosphere-free carrier-phase float ambiguities. Further, empirical accelerations in radial, along-track, and cross-track direction are estimated to compensate for deficiencies in the deter-ministic model. A more detailed descrip-tion of the POD procedure and the orbit model can be found in the article by O. Montenbruck et alia cited in Additional Resources.

As a more precise reference solution or independent measurements from satel-lite laser ranging are not available, a direct assessment of the errors in the precise orbits

is not possible. Therefore, orbit overlap comparisons are used here to yield at least an indication of the orbit quality. For this purpose, we computed 19 orbit solutions based on a data arc of five hours on August 30, 2012. The first hour of each orbit solu-tion with the central hour of a previous orbit solutions, starting two hours earlier.

The mean values of the pseudorange residuals are consis-tently between 70 and 75 centimeters for all 19 POD runs. The carrier-phase residuals are two orders of magnitude smaller and vary between 7.5 millimeters and 8.5 millimeters. Figure 7presents the results for the 3-D RMS overlap errors. The maxi-mum and minimum errors are 35 millimeters and 5 millime-ters, respectively, with an average of 18 millimeters.

FIGURE 6 Accuracy of NOX navigation solution compared to a pre-cise reference orbit for August 30, 2012.

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cross-tr. 0.52 0.56 0.77 3.00 -2.53

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TABLE 1. Statistics of errors of the PolaRx2 navigation solution for a 24h data set on August 30, 2012. Listed in the table are the mean and standard-deviation, the rms error, maximum and minimum errors

FIGURE 7 Statistics for POD RMS overlap errors in radial, along-track and cross-track direction based on 19 orbit solutions with a data arc 5 h on August 30, 2012. The first hour of an orbit has been compared to the central hour of an orbit solution, which starts two hours earlier.

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Experience shows that the inclusion of empirical accelera-tions in the estimation leads to a reduced stiffness of the solu-tion, which allows the estimated trajectory to closely follow the observations. As a result, the overlap comparisons tend to be too optimistic and the true orbit errors can be expected to be larger. Based on experience of previous missions with dual-frequency GPS receivers, we would expect the achievable 3-D RMS accuracy to be on the order of decimeters or better.

Antenna Phase Pattern. Inspection of the carrier-phase residuals from the reduced dynamic orbit determination has revealed clear systematic effects with azimuth- and elevation-dependent variations. These systematic residuals are due to antenna phase pattern variations of the receiving antenna caused by the effect of the satellite’s structure on the antenna in the absence of a choke ring or ground plane.

An antenna phase-center variation pattern can be derived based on carrier-phase observations processed over a longer time interval. For this purpose, we grouped the residuals of the POD into azimuth and elevation bins depending on the direction of the received signal. The phase center variation cor-rection is then computed as the average of the residual in this bin. The resulting phase pattern correction is then used again for a POD and refined with corrections based on the residuals of further iterations.

Figure 8 depicts the results for the POD antenna based on the data of all NOX activations between August and December, 2012. The maximum amplitude of phase center variations is ± 25 millimeters. The phase pattern exhibits an irregular shape with rapid changes between maximum and minimum varia-tions. The standard deviation of the carrier-phase residuals in the POD can be reduced from 12 millimeters to 8 millimeters using this correction pattern.

Radio OccultationsDuring a radio occultation (RO), the signal of a GNSS satellite is tracked by a receiver on the opposite side of the Earth close to the horizon. Because the signal is received through the Earth’s atmosphere, it is affected by delays and bending depending on the refractivity of the atmospheric layer.

The refractivity can be approximated as a function of total-electron content in the ionosphere as well as temperature, pres-sure, and humidity in the troposphere. We can compute the bending angle and the corresponding ray height of the signal

from carrier-phase measurements, which allows us to retrieve the refractivity index and solve for atmospheric and ionospheric parameters.

Due to the change in geometry between the two satellites, the signal is received through different layers of the atmosphere during an occultation event. If high rate carrier-phase measurements are available, bending angle profiles for different alti-tudes can be recorded. The derived atmo-spheric parameters serve as input data for weather prediction and climate research, which are the main motivations for radio occultations.

Figure 9 presents a schematic of a radio occultation. The GNSS and the LEO satel-lite travel with velocities of vs and vr, respec-tively. The direct straight-line connection

FIGURE 9 Schematic of occultation measurements between a GPS and a LEO satellite, which travel with velocities vs and vr , respectively. The signal is bent by an angle α due to the iono-sphere and troposphere at the impact height a.

SLTA


FIGURE 8 Polar plot of carrier-phase pattern of the NOX POD antenna iteratively computed from carrier-phase residuals from the reduced dynamic orbit determination.

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between the LEO satellite and the GNSS satellite determines the straight-line tangent altitude (SLTA), which is the distance of this line to the surface of the Earth.

Due to the bending, the actual signal path does not follow this straight-line connection, but is curved around the Earth at an impact height a. This curvature is characterized by the bending angle α at the corresponding impact height. Due to the signal bending, the receiver can still track the GNSS satellite, even though it may be below the Earth’s horizon as seen from the LEO satellite. However, note that the bending depicted in the plot is highly exaggerated and does in reality not exceed one degree.

The fundamental relation to be solved for in RO processing is the dependency of the bending angle on the impact height. For this purpose, the carrier-phase measurements are cor-rected for geometrical range between LEO and GNSS satellite, receiver and GNSS satellite clock offsets, and relativistic effects, which only leaves the delays due to ionosphere and troposphere. This residual is referred to as the excess phase delay. The LEO orbit and the clock offset corrections are obtained from a POD using the occultation antenna. The bending angle can then be retrieved from the change in excess phase, referred to as the excess Doppler, and the LEO and GNSS satellite velocity.

Figure 10 shows results for an occultation event of GPS satel-lite SVN 47 (PRN 22) on September 2, 2013, between approxi-mately 17:42 and 17:48 UTC. The tangent point coordinates of the occultation are 28.18° S and 82.81° E, which correspond to a location in the Indian ocean, halfway between Madagascar and Western Australia. GPS measurements have been taken with a data rate of five hertz.

The top plot in Figure 10 shows the signal-to-noise density ratio (C/N0) for L1-C/A code and L2-P(Y) code together with the straight-line tangent altitude. The plot starts at an SLTA of approximately 500 kilometers, where the signal of the GPS satellite is tracked through the upper ionosphere. The C/N0 is practically constant at 46.6 dB-Hz and 33.3 dB-Hz for C/A and L2-P(Y), respectively, for almost the entire period of time. Only during the last 30 seconds of tracking at an SLTA of 50 kilome-

ters and less, the C/N0 starts to drop and exhibit larger varia-tions due to the signal attenuation in the lower atmosphere. Also note that the L2-P(Y) tracking is disrupted earlier than L1 C/A.

The bottom of Figure 10 plot shows the corresponding excess carrier-phase for L1 and L2 together with the slant ion-ospheric delay computed from dual-frequency carrier-phase measurements. The slant ionospheric delay for the L1 frequency IL1 has been computed from

In this equation, and are the L1 and L2 frequency, respectively, and and are the carrier-phase measure-ment in units of length. This geometry-free carrier-phase combination removes all frequency-independent terms such as geometry, clock offsets, and tropospheric delay. The delay has the positive sign convention of the pseudorange delay, even though it has been computed from carrier-phase mea-surements. Note that the ambiguities and frequency-dependent signal delays do not cancel out in Equation 1. Therefore, the absolute value of the ionospheric delay cannot be recovered, but only the temporal variation.

As expected, the ionospheric delay is smallest at high alti-tudes, where the electron content is low. As the signal path proceeds into the lower ionosphere, the delay increases and reaches a peak at a straight-line altitude of approximately 300 kilometers. For lower tangent altitudes, the delay decreases again. The maximum amplitude of the ionospheric delay varia-tion over the data arc is about five meters, or ~30 total electron content (TEC) units. Since the measurements were taken on the dark side of the Earth, the ionospheric electron content is low.

The excess phases for L1 and L2 depicted in the bottom plot of Figure 10 show a maximum amplitude of ~240 meters for a straight line tangent altitude of -20 kilometers, where the signal is tracked through the lower part of the atmosphere. At higher altitudes, only small variations of the excess phases due to the ionospheric delays are present. As a result, the bending angle

FIGURE 10 Measurements of an occultation event for GPS satellite SVN 47 (PRN 22) on September 2, 2013. The top plot depicts the carrier-to-noise density ratio for L1 and L2 measurements as well as the straight-line tangent altitude (SLTA). The bottom plot shows the excess carrier-phase measurements for L1 and L2, as well as the ionospheric delay for the L1 frequency.

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is small at high altitudes and increases rapidly as soon as the signal path enters the troposphere.

If the excess phases of Figure 10 are converted into excess Dopplers, the bending angle of the signal can be computed as a function of the impact height. Figure 11 depicts the correspond-ing results for the L1 and L2 frequency. The plot shows that the bending angle for the L1 and L2 frequency differ due to the frequency-dependent ionospheric delays. In order to remove this effect, an ionosphere-free (or neutral) bending angle (LC) has been computed as a linear combination of the L1 and L2 bending angles at the same impact height.

The minimum impact height for this occultation is approximately 7 kilometers. The bending angle at this height is approximately 0.6 degree. For higher altitudes, the bending angle decreases until it reaches a level of about 0.001 degree for impact heights larger than 50 kilometers. The bending angles exhibits only very low noise for impact heights lower than 40 kilometers.

At higher altitudes, the noise increases significantly. The reason for the different noise levels becomes clear from the plot of the excess phases in Figure 10. For high altitudes, the signal delays are small, and measurement noise and model imper-fections dominate. Apparently, bending angles less than 0.001 degree cannot be observed, because they are below the noise floor in the current processing. When the signal crosses the troposphere, the delays grow quickly with decreasing impact height and become distinguishable from the noise.

Summary and ConclusionsThis article as presented initial flight results of the Naviga-tion and Occultation eXperiment on-board the small satellite TET-1. The experiment has demonstrated that commercial-off–the-shelf hardware can be used in space-borne applications, with only minor changes to the receiver’s firmware.

With the height and velocity constraints removed and an increased Doppler search window, the receiver has reliably acquired and tracked sufficient satellites for a continuous navi-gation solution, typically within less than three minutes. The 3-D RMS errors of the navigation solutions are on the order of a few meters only. No latch-ups or other receiver failures have been observed during the entire mission.

Plots of the C/N0 variation and the phase pattern variations in the antenna diagram indicate an effect of the satellite’s struc-ture on the antenna characteristics. If a ground plane or choke ring is used to mount the antenna, these effects can be expected to be less pronounced.

In the absence of a more precise reference solution or inde-pendent measurements, for example from satellite laser rang-ing, the precise orbit determination accuracy cannot be directly assessed. Orbit overlap comparisons have shown errors of a few centimeters, between the first hour and the central hour a two five-hour orbit arcs. The use of an empirically derived correction pattern for phase center variation could reduce the carrier-phase residuals of the POD, typically from 12 millimeters to 8 mil-limeters.

Radio occultation experiments have shown that dual-frequency carrier-phase signals can be tracked through the Earth’s troposphere with a data of five hertz. The data enables researchers to monitor the ionospheric delay and derive slant TEC variations of the upper atmosphere. A bending angle pro-file for L1 and L2 carrier-phase measurements has been derived in the troposphere down to an impact height of about seven kilometers.

The NOX experiment proves that a low-cost GPS system, which fulfills the requirements for precise orbit determination, can be realized with COTS hardware. This approach may be appealing for research groups seeking to gain inexpensive access to relevant data and even help to identify possibilities for cost reduction in future satellite missions.

Analysis of NOX RO observations has demonstrated that GPS signals could be tracked through the ionosphere and tro-posphere below the satellite’s horizon. We must note, however, that the performance of this setup not sufficient to produce RO data ready to be used for use in weather forecast or climate research. Several special modifications — such as open-loop tracking, an autonomous occultation prediction and channel allocation algorithm in the receiver, and a higher sampling rate as well as a high-sensitivity antenna system — would be needed to make this system competitive to modern RO payloads.

AcknowledgmentsThe authors would like to acknowledge the contributions of


FIGURE 11 Bending angle profile for an occultation event for GPS satellite SVN 47 (PRN 22) on September 2, 2013. Depicted are the L1 and L2 bending angles and a ionosphere-free combination (LC) of bending angles.

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their former colleagues Cécile Renaudie and Miquel Garcia-Fernandez, who have worked on the design and manufactur-ing of the NOX experiment. The support of colleagues at Kayser-Threde GmbH during the design, implementation, and operation of NOX payload is greatly appreciated. The team at Septentrio is acknowledged for technical support and discussions.

The authors would also like to thank the TET operations team at the Ger-man Space Operations Center, especially Andreas Spörl, Andreas Pohl, and Jens Richter, for their help in planning and implementing the NOX experiments. DLR Space Administration is acknowl-edged for the free flight opportunity of the NOX experiment on-board TET-1.

This article is based primarily on a paper presented at the ION GNSS+ 2013 conference in Nashville, Tennes-see, USA.

ManufacturersThe NOX payload incorporates a PolaRx2 GPS receiver from Septentrio nv, Leuven, Belgium; two S67-1575-14 L1/L2 passive patch antennas from Sen-sor Systems, Inc., Chatsworth, Califor-nia, USA; and a Spectrum Microwave LNA 310-025105-011 low noise amplifier from API Technologies Corporation,Orlando, Florida, USA. An STR4760 sig-nal simulator from Spirent Communi-cations, plc, Paignton, United Kingdom, has been used during pre-flight testing and qualification of the receiver. The TET-1 satellite bus navigation system uses a Phoenix-S receiver by from DLR.

Additional Resources[1] Eckert, S., and S. Ritzman, J. Eckler, and W. Bärwald,” “On-Orbit Verification with a Technology Test Carrier TET,” in Proceedings of 6th IAA Sym-posium on Small Satellites for Earth Observation, Berlin, Germany, April, 23–26, 2007

[2] Föckersperger, S. and G. Staton, and M. Turk, “Future Small Satellite EO Missions Based on TET,” in Proceedings of the Small Satellites Systems and Services Symposium 2012, Portorož, Slove-nia, June 4–8, 2012

[3] Garcia-Fernandez, M., and O. Montenbruck, M. Markgraf, and J. Leyssens, “Affordable Dual-frequency GPS in Space,” in Proceedings of the

6th International ESA Conference on Guidance, Navigation and Control Systems, Loutraki, Greece, October 17–20, 2005

[4] Gleason, S., and D. Gebre-Egzabher, GNSS: Applications and Methods, Artech House, Nor-wood, Massachusetts, USA, 2004

[5] Hajj, G.A., and E. R. Kursinski, L. J. Romans, W. I. Bertiger, and S. S. Leroy, “A technical descrip-tion of atmospheric sounding by GPS occultation,” Journal of Atmospheric and Solar-Terrestrial Physics, 64:451–469, 2002. doi: 1364-6826/02/

[6] Kursinski, E. R., and G. A. Hajj, J. T. Schofield, R. P. Linfield, and K. R. Hardy, “Observing Earth’s atmosphere with radio occultation measurements using the Global Positioning System,” Journal of Geophysical Research, 102(D19):23,429–23,465, 1997. doi: 0148-0227/97/97 JD-01569

[7] Lemke, N. M. K., and C. Kaiser, S. Föcker-sperger, G. Staton, and T. Stuffler, “TET-Based Small Satellite Family,” in Proceedings of the 63rd International Astronautical Congress, Naples, Italy, October 1–5, 2012

[8] Leyssens, J., and and M. Markgraf, “Evaluation of a Commercial-Off-The-Shelf Dual-Frequency GPS Receiver for Use on LEO Satellites,” in Pro-ceedings of the ION GNSS, Long Beach, California, USA, September 13–16, 2005

[9] Markgraf, M., and C. Renaudie, and O. Mon-tenbruck, “The NOX Payload-Flight Validation of a Low-Cost Dual-Frequency GPS Receiver for Micro- and Nanosatellite Applications,” in Proceedings of the IAA Symposium on Small Satellite Systems and Services (4S), Rhodes, Greece, May 26–30, 200.

[10] Markgraf, M., and P. Swatschina, “The Navi-gation and Occulation eXperiment (NOX) onboard TET-1,” presented at 2nd TET Customer Day, Kay-ser-Threde, Munich, Germany, July 5, 2010

[11] Melbourne, W.G., and E. S. Davis, C. B. Dun-can, G. A. Hajj, K. R. Hardy, E. R. Kursinski, T. K. Meehan, L. E. Young, and T. P. Yunck, The Appli-cation of Spaceborne GPS to Atmospheric Limb Sounding and Global Change Monitoring, JPL Publication 94-18, 1994

[12] Montenbruck, O.,l and T. van Helleputte, R. Kroes, and E. Gill, “Reduced dynamic orbit determination using GPS code and carrier measurements,” Aerospace Science and Tech-nology, 9(3):261–271, 2005. DOI 10.1016/j.ast.2005.01.003

[13] Ware, R., and M. Exner, D. Feng, M. Gorbunov, K. Hardy, B. Herman, Y. Kuo, T. Meehan, W. Mel-bourne, C. Rocken, W. Schreiner, S. Sokolovskiy, F. Solheim, X. Zou, R. Anthes, S. Businger, and K. Trenberth, “GPS Sounding of the Atmosphere from Low Earth Orbit: Preliminary Results,” Bulletin of the American Meteorological Society, 77, 19–40.

DOI 10.1175/1520-0477(1996)077<0019:GSOTAF>2.0.CO;2

[14] Yoon, Z., and T. Terzibaschian, C. Raschke, and O. Maibaum, “Robust and Fault Tolerant AOCS of the TET Satellite,” in Proceedings of the 7th IAA Symposium on Small Satellites for Earth Observa-tion, Berlin, Germany, May 4–7, 2009

AuthorsAndré Hauschild is a mem-ber of the scientific staff of the GPS Technology and Navigation Group at DLR’s German Space Operations Center (GSOC). His field of work focuses on real-time

precise clock estimation for GNSS satellites as well as multi-GNSS processing using modernized GPS and new satellite navigation systems. He is also involved in projects with space-borne GNSS receivers for scientific applications like precise orbit determination and radio occultation. André graduated in aerospace engineering from Tech-nische Universität Braunschweig, Germany, and received his Dr.-Ing. from the Technische Univer-sität München, Germany.

Markus Markgraf is a senior research engineer in the GNSS Technology and Navigation Group at DLR/GSOC. He started working at DLR in 2000 after grad-uating as a Dipl.-Ing. (FH)

for electrical engineering and communication technology. His current research activities com-prise GNSS receiver technology for satellites and sounding rockets, scientific applications of GNSS, and mission support and analysis. He was the key engineer for the Navigation and Occultation Experiment (NOX) on TET-1 during the design and implementation phase.

Oliver Montenbruck is head of the GNSS Technology and Navigation Group at DLR’s German Space Oper-ations Center (GSOC), Oberpfaffenhofen. His cur-rent research activities

comprise spaceborne GNSS receiver technology, autonomous navigation systems, spacecraft for-mation flying, and precise orbit determination as well as new constellations and multi-GNSS pro-cessing. Dr. Montenbruck presently chairs the GNSS Working Group of the International GPS Ser-vice and coordinates the performance of the MGEX Multi-GNSS Experiment. He has authored numer-ous technical papers and various textbooks.


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In recent years, numerous, relatively inexpensive hardware platforms for conducting scientific research using

the software defined radio (SDR) para-digm have become commercially avail-able. The Manufacturers section near the end of this article lists examples of several of these. In turn, this has spurred universities and research groups around the world to adopt this technology for advanced GNSS signals-based research and development.

Popular research topics exploit-ing GNSS SDR receivers include “first look” GNSS signal capture and analy-sis, interference/spoofing detection and mitigation, GNSS signal authentication by means of nominally present satellite signal distortions (i.e. signal “fingerprint-ing”), signal quality and deformation monitoring, GNSS bi-static radar and synthetic aperture radar (SAR)-based imaging, multi-platform combined GNSS signal processing, advanced GNSS mul-tipath mitigation, multi-element phased array processing, ultra-tight integration of GNSS with multiple sensors, vector tracking loops and other “holistic” and “open loop” signal tracking approaches, ionospheric research using multi-fre-quency GNSS observables, and general multi-constellation/multi-frequency GNSS receiver development, prototyping, testing and algorithm validation.

The general approach to carrying out such research involves one or more data collection campaigns followed by mul-

tiple cycles of algorithm development, sampled data processing, and analysis. Realtime processing capability is gener-ally not required at this stage of devel-opment. However, to achieve maximum productivity researchers find it highly desirable to have flexibility in algorithm development by way of high-level pro-gramming languages and robust user-friendly development environments with extensive built-in math library sup-port and data visualization capabilities. Arguably, within the satellite navigation community, MATLAB has become the de facto standard in this regard.

Many of the a fore-mentioned research topics can involve sampled sig-nal data collection at wide bandwidths, high dynamic range, and multiple coher-ently sampled streams. For example, consider a wideband GNSS data collec-tion campaign for investigating phased array based interference mitigation tech-niques using a seven-element, controlled reception pattern antenna (CRPA). In this case, bandwidth, dynamic range, and multiple channels are all in play.

Assuming typical front-end hard-ware specifications for such an appli-cation of 60 megasamples per second, 14-bit samples (extended to two bytes for data transfer) and eight channels (one channel being a separate reference antenna), the data capture rate equals 960 Mbytes/second. Even with lesser requirements, it is not uncommon to return from a collection campaign with

Use of the software defined radio paradigm for GNSS receiver design and associated research are proliferating rapidly as computer processing power increases and costs decline. However, diverse approaches to the software employed for the high-level development environments of these designs limit the cross-platform utility and full exploitation of their computing platforms. The article describes the latest version of a universal GNSS SDR processing toolbox that is distributed as a plug-in for high-level algorithm development.

SANJEEV GUNAWARDENA

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www.trimbledimensions.com [email protected]


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multiple hundreds of gigabytes (if not terabytes) of data. Thus, a solution is needed to process such large datasets in a reasonable amount of time.

Today’s general-purpose desktop and laptop computers provide tremendous numerical computation capability at low power and cost. This is made pos-sible with multiple processor cores — each clocking at multiple gigahertz and supporting wide single-instruction-mul-tiple-data (SIMD) instructions. In addi-tion, today’s affordable consumer-grade solid-state drives feature sustained read speeds on the order of 500 megabytes/second. Hence, these machines are good candidates for crunching through large amounts of SDR data. For further dis-cussion about computational workloads, see the sidebar, “Cost/Benefit Justifica-tion for a GNSS SDR Toolbox.”

Unfortunately, the layers of software abstraction built into high-level devel-opment environments to facilitate user-friendly coding is one of the main rea-sons why, in general, these tools cannot take full advantage of the computation capabilities of the platforms they run on. Thankfully, all such tools support extensions to allow users to integrate

their own custom libraries written in low-level code. In MATLAB, this exten-sion framework is known as “MATLAB executable” (MEX).

The goal of the work reported in this article is to develop a truly uni-versal GNSS SDR processing toolbox for education and research that could be distributed in the form of a plug-in for high-level algorithm development platforms — specifically MATLAB. The following high-level features were envi-sioned for the toolbox:

range of cutting-edge GNSS signals-based research topics as described previously

and front-end frequency plans

GNSS signal structures and other signals of opportunity

methodology that is easy to learn and apply to the various research topics described here

-tional examples as possible, thus shortening the learning curve for both beginners as well as advanced users.

The work described in this article achieves, to a large extent, all of these objectives and, more importantly, builds the framework for the baseband signal-processing layer of a truly universal GNSS SDR architecture. The toolbox has been used successfully to process the following open GNSS signals using live data: GPS L1 C/A, GPS L2C, GPS L5, GLONASS FDMA signals on L1 and L2, Galileo E1 CBOC signals using BOC(1,1), BOC(6,1) and CBOC(6,1,1/11) processing; Galileo E5a and E5b, BeiDou B1, satellite-based augmentation systems (WAAS and EGNOS), and WAAS sig-nals on L5.

The software is currently distributed as a MATLAB toolbox and can be down-loaded free of charge for education and research use.

One important note: this toolbox is not a complete GNSS receiver in the sense that it does not output position, navigation, and time (PNT) solutions. However, the processed-signal outputs (available at a one-kilohertz rate) contain all the information needed for subse-quent processing of PNT solutions.

Supporting Multiple GNSS SDR File Formats Most SDR data collection systems store their IF-sampled or baseband-sampled data in binary format. For uninterrupt-ed collections over prolonged intervals, data are sometimes written to multiple small files because such a strategy allows files to be managed more effectively than one file written to a large-capacity vol-ume. For systems that collect SDR data continuously for the purpose of record-ing rare anomalous signal events, this multi-file collection strategy allows older files to be deleted to make space for new ones, thus extending the avail-ability of past history to the size of the storage array in contrast to the capacity of a memory-based buffer.

In some systems, the GNSS samples may be interlaced with binary data from other sensors such as IMUs, laser scanners or cameras to achieve inher-ent time synchronization between these sensors. In this case, additional metadata information is needed to extract GNSS samples from the file and,

Cost/Benefit Justification for a GNSS SDR ToolboxFor GNSS SDR, the most numerically intensive computations involve correlation of hundreds of millions of signed integer samples for each second of processing. However, these samples are typically less than one byte. Through some straight-forward pre-processing steps to reduce dynamic range, the result of correlation over a one-millisecond interval can usually be made to fit within 16-bit signed integers with negligible loss of performance.

Hence, these structurally regular fixed-point computations can be parallelized by factors of 8 or 16 using 128-bit and 256-bit wide Streaming SIMD Extensions (SSE) or Advanced Vector Extensions (AVX), respectively. (AVX has been sup-ported in all x86 processors shipping since 2011.) Further parallelization over the available number of logical processors (up to 8 in most consumer PCs) can yield up to 128× theoretical performance improvement compared to un-optimized code.

Such optimizations require the correlation algorithm to be partitioned so that subsets of the computations can be performed independently in each processor. This type of “fine-grained” architecting of an algorithm to exploit the feature set of a particular generation of processors to the maximum extent possible is best done by human programmers as opposed to optimizing compilers.

Because sample correlation is such a critical component of any GNSS SDR and the algorithm essentially does not change significantly with sampling rate or GNSS signal structure, the cost of low-level optimization can be justified by considering the subsequent time savings that can be gained. This is especially true if the correlation engine can be architected such that it supports a wide range of applications and use cases.

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optionally, decode the sensor data.Currently no standard exists within

the PNT community that allows GNSS SDRs to work seamlessly with files writ-ten by any SDR data-collection system. This means that the user is forced to set data decoding parameters in an ad hoc manner. When files from a different sys-tem are sourced, these parameters and the decoder must be changed manu-ally — a process that is prone to human error.

Part of the effort described in this article aims to address this issue so that files from any data collection system can be seamlessly integrated into any GNSS SDR processing platform. The proposed solution is to pair a metadata file with each binary data file. The metadata file includes all the information needed to integrate the SDR file into the processor and decode its contents.

As the format for the metadata file, eXtensible Markup Language (XML) provides a desirable option. All oper-ating systems and application develop-ment suites support XML, which is a low-overhead human-readable format, thus providing a straightforward process to integrate it into any data collection system. Figure 1 shows an example of an SDR metadata file written in XML. It contains all of the necessary information to decode the multi-stream samples cor-rectly as well as other information per-taining to the data collection campaign.

The SDR toolbox uses this meta-data mechanism to open and decode SDR data files from many data collec-tion systems. When opening a specified SDR file, the reader automatically parses the XML file and imports the metadata into the MATLAB workspace as a struc-ture. For multiple files, the user specifies the name of the first file along with the maximum number of one-millisecond blocks to be processed. This information is used to automatically find and splice the necessary files to fulfill the request.

Supporting a Wide Range of Research ApplicationsIn broad terms, GNSS baseband signal processing can be divided into three stages. The following sections sum-marize the features required in each of

these stages to support a wide range of research applications.

Pre-Correlation Processing. As is well known, correlation losses become neg-ligible for sample quantizations beyond two bits. However, this does not hold true in the presence of interference. In this case, we can use additional dynamic range to perform interference reduction processing prior to correlation. Typical pre-correlation processing includes sam-ple covariance computation (for interfer-ence detection and location) and digital filtering and excision techniques applied in the time and/or frequency domains.

The various types of pre-correlation processing that a researcher may want to apply to a GNSS processing application could be supported by including 1) an optimized sample statistics processor, 2) a sample masking processor for blank-ing interference-dominated samples from being correlated, 3) a configu-rable time-domain filter implementa-tion (such as Direct-Form II), and 4) a fast Fourier transform (FFT) engine for implementing frequency-domain inter-ference detection and excision tech-niques.

These processing blocks could be integrated into the sample streams using a software plug-in interface. Since imple-mentations already exist in MATLAB,

developing a fully featured pre-correla-tion processor was considered a lower priority compared to the correlation engine. However, Version 3 of the tool-box does include a sample statistics and noise processor as described in below.

Correlation Processing. Three fun-damental techniques exist for sample correlation: time-domain correlation, parallel frequency correlation, and par-allel code correlation. The latter two methods provide a large number of cor-relation outputs corresponding to Dop-pler frequency offsets or code phases, respectively.

The limited resolution of parallel correlation algorithms and the inability to steer the local replicas that produce them with adequate precision (par-ticularly with respect to code phase) preclude their use in precision signal tracking applications. The parallel code correlation algorithm is most efficient when researchers need a large swath of code correlation space observability such as during signal acquisition. Other uses include correlation space monitor-ing (also known as delay-Doppler map monitoring) for applications such as spoofer detection.

In any case, a low update rate on the order of one to several seconds is typi-cally sufficient for monitoring applica-

FIGURE 1 Proposed GNSS SDR metadata XML schema


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tions. As MATLAB already contains optimized FFT implemen-tations to write parallel correlation algorithms, no attempt was made in this version of the toolbox to accelerate FFT-based parallel correlators.

Many of the GNSS SDR research applications described here require several more time-domain correlators than the typical two to five needed for traditional signal tracking. Supporting a given processing algorithm for all current and future GNSS signals can become cumbersome due to their various signal structures.

The ability to instantiate any number of correlators per channel (where each channel can be setup for any GNSS signal structure), and have all these correlators and channels managed with little-to-no user intervention is one of the most desired features of a universal GNSS SDR. This is because it allows the researcher to focus on higher-level algorithm development without having to be concerned with correlator implementation details. Bringing this idea to fruition was one of the major goals and contributions of this effort. The following section describes the architecture of these universal GNSS correlators.

Post-Correlation Processing. Following sample correlation, the data rate is reduced to an easily handled value of one kilo-hertz. Most of the specialized GNSS signal processing algo-rithm development occurs in this post-correlation domain. This is also where the strengths of high-level algorithm devel-opment tools such as MATLAB shine in terms of a researcher being able to modify scripts and visualize the effects quickly and easily.

An SDR toolbox must feature an interface to and from this domain that is both efficient and intuitive in terms of configur-ing and controlling the various types of channels and correla-tors as required by the researcher.

Functional ArchitectureThis article serves as an introduction to Version 3 of the GNSS SDR toolbox. This version’s functional architecture is signifi-cantly different to that of the previous version (v2) that was described in the paper by S. Gunawardena (2013) listed in Addi-tional Resources.

Figure 2 shows the high-level functional block diagram of the GNSS SDR toolbox for MATLAB. Sampled data streams are read from source SDR data files, followed by buffering and decoding into one or more data streams. The streams are fed into two main signal-processing blocks: a stream statistics and

noise processor, and a multi-channel ChipShape correlation engine.

To maintain a regular channel architecture that is not spe-cific to any GNSS signal structure, the toolbox uses memory codes exclusively for all pseudorandom noise and masking sequences. These codes are fetched from files and saved in a cache that is accessible to both processing blocks. This code cache is fully configurable by the user such that unused codes can be swapped out for new ones at runtime.

The stream statistics and noise processor computes sample means, variances, and histograms for every one-millisecond block of samples. Sample statistics provide a valuable low-laten-cy “situational awareness” indication of in-band interference. Researchers can use the raw one-millisecond outputs of this processor to prototype a range of interference detection/moni-toring algorithms. The toolbox includes commands to disable these computations if not used.

In GNSS receivers, a channel control state machine is typi-cally used to handle the transition from acquisition to steady-state tracking (and subsequent reacquisition to tracking fol-lowing loss-of-lock events). A low-latency signal-to-noise ratio (SNR) estimate is used as one of the inputs to this controller. Hence, the SNR calculation requires an estimate of noise power, in general for each sample stream.

Some receivers employ a spare channel to compute this noise estimate by correlating with a PRN sequence that is known to be absent in the data. The toolbox implements these noise cor-relators within the stream processor block. To reduce computa-tion load, noise correlators implement only the real component, and the numerically controlled oscillator (NCO) phase register sizes are also smaller than those used for tracking channels.

As with the statistics processes, each noise correlator can be turned off to improve runtimes. Because these noise correla-tors can be set to correlate with any of the configured memory codes (including for example, a dedicated random-noise code of any length), the likelihood of significant cross-correlation with in-band signals can be minimized.

Version 2 provided instantiation of any number of correlator

FIGURE 2 GNSS SDR Toolbox Version 3 high-level functional block diagram

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The ability to instantiate any number of correlators per channel (where each channel can be setup for any GNSS signal structure), and have all these correlators and channels managed with little-to-no user intervention is one of the most desired features of a universal GNSS SDR.


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points per channel, where each could be connected to one or more universal code generators with independently variable relative code phase delay. Even though this architecture facilitates a wide range of applications, this earlier version repeated the underlying sample-level multiply-accumulate operations when points were placed with less than one-chip separation from each other.

Version 3 eliminates these repeated operations by natively performing Chip-Shape correlation on an array of points. Ranges of points from this ChipShape output vector can be combined (at the user level) to form any desired points of the traditional triangular correla-tion function. For binary offset carrier (BOC) signals, the need for a subcarrier replica in the correlation process is also eliminated because the user can apply any subcarrier function as part of the ChipShape-to-triangular conversion step described previously. Further, by applying chip masking patterns that are specific to the spreading code (com-bined with long coherent integration), transients of the underlying signal can be observed at high fidelity.

This technique has applications in advanced multipath mitigation, signal quality monitoring and authentication. The paper by S. Gunawardena et alia(2012) listed in Additional Resources provides an overview of ChipShape processing, the concept of chip mask-ing, and its applications in signal quality monitoring.

Figure 3 shows the functional archi-tecture of a Version 3 channel. As with previous versions, the user can instanti-ate any number of these channels in the toolbox. The main user-configurable channel parameters are shown in red.

The Stream Index parameter selects the input data stream to be processed by a channel. Carrier wipeoff is performed on this selected stream using the replica generated by the carrier NCO — con-trolled by phase-rate commands updat-ed each millisecond. The carrier-wiped stream is then sent to independent banks of correlators that perform ChipShape correlation for a one-millisecond block of samples. The result is a ChipShape vector for each bank that is transferred to the

user space (i.e., MATLAB workspace).Each ChipShape bank is configured

independently by means of three param-eters: the number of correlation points per chip NC, the whole number of chips spanning to the early side NE (relative to code NCO integer phase), and the whole number of chips spanning to the late side, NL. Hence, the size of the Chip-Shape vector is given by NC·(NE + NL + 1),and the spacing between points is given by 1/NC.

As shown in Figure 3, ChipShape processing essentially splits traditional correlation into partial accumula-tions, where the fractional state of the code NCO determines the array index applicable to the partial accumulation being processed. Splitting the correla-tion operation in this way maximizes opportunities for these accumulations to be combined in user space to form numerous correlation and/or code dis-criminator functions depending on the application. Another welcome benefit is that this method reduces the dynamic range required to prevent overflow of these accumulators by a factor of 1/NCcompared to a traditional correlator.

Not shown in Figure 3 are the three levels of enable/disable logic featured in the toolbox to improve runtimes: 1) enable/disable entire channels that were instantiated (also disables chan-

nel NCOs), 2) enable/disable banks that were instantiated within a channel, and 3) enable/disable individual points with-in a given bank.

If a ChipShape correlator is imple-mented as described thus far, the output vector would simply be the differential of a traditional triangular correlation function. Although useful, it does not provide full insight into chip transition edges and their precise zero crossings. The rising, falling, and stationary parts of a GNSS signal’s underlying code sequence can be recovered by correlat-ing with a local replica that corresponds only to these events (e.g., to recover the rising-edge, keep all -1 to +1 chip transi-tions in the code sequence and set others to zero).

As shown in Figure 3, this function-ality is implemented by multiplying the carrier-wiped sample stream with an optional masking sequence. (In actual-ity the mask bit is used to disable accu-mulation for that sample.) Each bank is configured independently to point to any code and/or mask sequence stored in the Code Cache shown in Figure 2.

Applying the ChipShape CorrelatorThe native ChipShape correlator architecture of Version 3 significantly expands possibilities for advanced GNSS

FIGURE 3 Functional architecture of a Version 3 ChipShape correlator channel


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signals-based research beyond what was possible with Version 2. Among these are the following examples.

Built-In Acquisition and Rapid-Reac-quisition. A dedicated channel can be instantiated for acquisition and/or rapid reacquisition. This channel’s ChipShape vector would span a significantly larger range of integer chip offsets with coarse inter-point spacing (e.g. NC=2 for BPSK, NC=4 for BOC(1,1), and so on). A given PRN can be acquired by pointing to the corresponding memory code and pro-gressively searching for sets of code phase offsets and Doppler frequencies over time.

For reacquisition, the channel’s car-rier and code NCOs are set with best estimates of Doppler frequency and codephase, respectively. In this case, ChipShape points corresponding to unnecessary span may be disabled to reduce runtimes. An estimate from the noise processor can be used as the basis for setting up the acquisition detection threshold.

Spoofer Monitoring. A civilian GPS spoofing scenario described in the paper by T. E. Humphreys et alia (Additional Resources) attempts to pull a receiver tracking channel away from the genu-ine signal’s correlation peak by coercing it to lock on to a stronger peak produced by the spoofer. Even if Doppler offset and codephase are perfect matches to the genuine signal, the superposition of the two would cause significant dis-tortion of the ChipShape function due to the spoofer’s RF transmitter transfer function (which itself is a function of its characteristic analog RF components including modulators, amplifiers, filters and antenna).

A monitor/detector could be imple-mented using the GNSS SDR toolbox based on a high-resolution ChipShape output computed by an additional bank in each channel. To reduce runtimes (which corresponds to reducing power in a practical application), this bank can be activated at periodic intervals or at the onset of in-band noise power fluc-tuations (as monitored by sample vari-ance and/or noise correlators), which is a “cheaper” first indicator of possible in-band interference.

Chip Edge–Based Code Tracking for

Advanced Multipath Mitigation. As evident from the ChipShape functions shown in the examples section, zero-crossing rising-edge and falling-edge transitions are the highest-frequency components attainable from any received GNSS sig-nal through correlation processing. This is true regardless of signal structure. Hence, code tracking techniques that are primarily based on these transitions stand to produce the best pseudorange accuracy and multipath mitigation per-formance possible for any receiver of that bandwidth. Researchers can use the highly configurable ChipShape outputs produced by this toolbox as an enabler for researching novel edge-based code tracking techniques for precision GNSS applications.

GNSS Signal Authentication. Variations present in signal transmission payloads of satellites are known to cause subtle signal deformations that are detectable using appropriate processing techniques. Not surprisingly, ChipShape functions are the cornerstone of these techniques. For authentication applications, the deformation caused only by the satellite payload (as a function of nadir angle) must be isolated from nuisance compo-nents that include multipath, receiver antenna/front-end transfer function (including any variations due to tem-perature, vibration, and aging), and ionospheric effects.

Signal-Processing ApplicationsThis section provides two GNSS signal-processing examples that illustrate the configuration and capabilities of the toolbox.

Tracking and Eye Diagram Extraction for BPSK(1) Signals: GPS L1 C/A. For this example, a Version 3 channel was con-figured with five banks as follows:

C=120, NE=NL=1, Code: “GPS C/A PRN,” Mask: “None”

C=120, NE=NL=1, Code: “GPS C/A PRN,” Mask: “GPS C/A PP PRN”

C=120, NE=NL=1, Code: “GPS C/A PRN,” Mask: “GPS C/A PN PRN”

C=120, NE=NL=1, Code: “GPS C/A PRN,” Mask: “GPS C/A NP PRN”

C=120, NE=NL=1, Code: “GPS C/A PRN,” Mask: “GPS C/A NN PRN”

where PRN is the C/A code PRN number used and “PP,” “PN,” “NP,” and “NN” correspond to masking codes in which adjacent positive (P) and negative (N) chip events are isolated from the origi-nal C/A code. (The distribution includes a utility that generates these and other masking code files from a given PRN code file.)

The GPS L1 C/A signal was pre-acquired using the FFT-based “Quick Acquisition” utility included in the distribution. The latest distribution includes a fully open-source, single channel–tracking script that features a built-in tracking state controller. This state machine was configured to pull-in from acquisition, perform bit synchro-nization, and settle with the following steady-state tracking loop parameters: 20 milliseconds pre-detection integra-tion time, 18-hertz, third-order phase locked loop (PLL) bandwidth, one-hertz carrier-aided first-order delay locked loop (DLL) bandwidth, and coherent early-minus-late code phase discrimi-nator with early-late correlator spacing of 0.0167 chips.

The final state activates banks 2 thru

derived from Bank 1 (i.e., sign[Prompt-Q]) to keep rising, falling, positive, and negative components of the underlying signal together, the one-millisecond ChipShape outputs are coherently com-bined for approximately 100 seconds. Accompanying figures show the result-ing normalized ChipShape outputs.

Figure 4 shows the GPS C/A code eye diagram from a GPS front-end module with approximately four megahertz bandwidth. The effect of narrow front-end bandwidth compared to the results depicted in the following two figures is clearly evident.

Figure 5 and Figure 6 show eye dia-grams for GPS Block IIF-6 (SVN67 PRN06) at 78-degree elevation processed from data obtained with the TRIGR GNSS data collection system developed by the Ohio University Avionics Engi-neering Center. The final-stage IF filters for these two data streams included a

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transversal surface acoustic wave (SAW) filter with 24-megahertz/3-decibel band-width and a lumped element elliptic response filter comprised of a series of coaxial bandpass filters with 3-decibel bandwidth of 18 megahertz.

The bandwidth is sufficiently high in both eye diagrams to observe the 10 cycles of ripple that occurs within a C/A chip. As described in the article by S. Gunawardena et alia (2012b), these oscil-lations have been determined to be cross-talk from the P(Y) code modulation.

Careful observation of time intervals just prior to a chip transition in Figure 6 reveals a slight buildup of power. This effect, not observable in Figure 5, is primarily due to the finite impulse response-type characteristic of trans-versal SAW filters as will be reported in detail in a forthcoming presentation by S. Gunawardena et alia (2014) at the ION GNSS+ in September.

Tracking and E1C /E1B Subcarrier Extraction for CBOC(6,1,1/11) Signals: Galileo E1. For this example, a Version 3 channel was configured with four banks as follows:

C=120, NE=NL=1, Code: “GAL E1C PRN,” Mask: “None”

C=120, NE=NL=1, Code:

C=120, NE=NL=1, Code: “GAL E1C PRN,” Mask: “GAL E1C FF PRN”

C=120, NE=NL=1, Code:

FF PRN”where “FF” corresponds to masking sequences where adjacent chips with the same sign are isolated from the original spreading code.

-nal tracking and data symbol extraction, respectively. The ChipShape outputs from these banks are correlated with

produce traditional early, prompt, and

4 are initially deactivated. After steady-state tracking is reached, the ChipShape

-ently integrated for approximately 100 seconds by performing symbol wipeoff using the known overlay symbols and the

FIGURE 4 GPS L1 C/A signal eye diagram processed from a front-end with ~4-MHz pre-corre-lation bandwidth

ChipShape, GPS PRN28, Bw=4MHz

Relative CodePhase [chips]

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FIGURE 5 GPS L1 C/A eye diagram processed from a front-end with 18-MHz pre-correlation bandwidth

ChipShape, GPS BLK IIF-6 (PRN6) EI=78 deg. Bw=18MHz


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FIGURE 6 GPS L1 C/A eye eiagram processed from a front-end with 24 MHz pre-correlation bandwidth

ChipShape, GPS BLK IIF-6 (PRN6) EI=78 deg. Bw=24MHz


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data symbols derived from the prompt correlator of Bank 2, respectively.

Similar to the GPS C/A code tracking example, the channel state machine was configured to obtain the same steady-state tracking parameters. However, instead of the bit synchronization state used for GPS C/A code, the Galileo E1 tracking demo uses the included overlay code synchronizer.

Figure 7 shows the one-millisecond prompt correlator outputs (pilot and data) from the acquisition pull-in state to just after activation of banks 2–4.

Figure 8 and Figure 9 show the Gali-leo FM3 E1 CBOC(6,1,1/11) pilot and data component subcarriers as observed from front-end bandwidths of 18 and 24 megahertz, respectively. As to be expect-ed, the multi-level subcarrier functions experience more distortion with the 18-megahertz front-end compared to 24 megahertz. Also notice that for tra-ditional early-minus-late discriminator-based code tracking, zero crossings do not occur at zero codephase due to band-limiting.

ConclusionThis article introduced the GNSS SDR Toolbox for MATLAB (Version 3). This software performs GNSS SDR baseband signal processing using an optimized multi-threaded approach. The main motivation behind the development of this tool was to accelerate offline pro-cessing times for large GNSS SDR datas-ets. The toolbox improves runtimes by at least a factor of 30 compared to equiva-lent MATLAB-only scripts.

The main feature of Version 3 is a multi-channel universal GNSS Chip-Shape correlation engine that can be used as the foundation for advanced GNSS receiver development, algorithm design, and prototyping. It can also be used as an educational tool for demon-strating advanced GNSS signal process-ing techniques.

The Version 3 distribution contains numerous open-source scripts that demonstrate the setup and use of all major features. The toolbox is avail-able free of charge for educational and non-commercial research use. The software and additional resources are

FIGURE 7 From example of tracking and E1C/E1B subcarrier extraction for CBOC: one-millisec-ond Galileo E1C (pilot) and E1B (data) prompt correlator outputs over time

Galileo FM3 1 ms Prompt Correlator Outputs

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FIGURE 8 Galileo FM3 CBOC(6,1,1/11) E1C and E1B subcarrier functions processed from a front-end with 18 MHz pre-correlation bandwidth

Galileo FM3 CBOC(6,1,1/11) SubCarrier. EI: 85 deg. BW: 18 MHz


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FIGURE 9 Galileo FM3 CBOC(6,1,1/11) E1C and E1B subcarrier functions processed from a front-end with 24 MHz pre-correlation bandwidth

Galileo FM3 CBOC(6,1,1/11) SubCarrier. EI: 85 deg. BW: 24MHz


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RECEIVER TOOLBOX


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available through the author’s blog: <ChameleonChips.com>. Minimum software requirements needed to run the toolbox include Microsoft Windows (32 or 64-bit) and MATLAB version 2007B or above.

AcknowledgmentThis article was adapted in part from a presentation given by the author at the ION GNSS+ 2013 conference on Sep-tember 19, 2013. The views expressed in this article are solely those of the author and not those of any other person, insti-tution, organization, or entity.

ManufacturersThe MATLAB toolbox described in this article was developed using Microsoft Visual Studio from Microsoft Corpora-tion, Redmond Washington USA. The software was profiled using Intel Paral-lel Studio from Intel Corporation, Santa Clara California USA. The software runs on, and plots for this article were gener-ated using MATLAB from The Math-works, Inc. Natick, Massachusetts, USA. The operating system used was Windows 7 64-bit from Microsoft Cor-poration, Redmond Washington, USA. Narrowband SDR data for the example presented in the section titled “Signal-Processing Applications” were collected using a SiGe GN3S Sampler V3 from Sparkfun Electronics, Boulder, Colo-rado USA. Wideband GPS L1/Galileo E1 data were collected using a TRIGR GNSS data collection system from the Ohio University Avionics Engineering Cen-ter, Athens, Ohio USA. The final-stage IF filters for the two data streams incor-porated a SAWTEK 854672 transversal SAW filter from TriQuint Semiconduc-tor Inc., Hillsboro, Oregon, USA, and a lumped element elliptic response filter comprised a series of six SBP-70+ coax-ial bandpass filters from Mini-Circuits,Brooklyn, New York, USA.

Examples of low-cost data collection hardware platforms that support GNSS bands include the SiGe GN3S Sampler (see Sparkfun Electronics publication, Additional Resources), the Univer-sal Software Radio Peripheral (USRP) (Ettus Research in Additional Resourc-es), and products based on fully inte-

grated field programmable RFICs such as the Loctronix ASR-2300 (Loctronix Corporation, Additional Resources) and bladeRF (Nuand, Additional Resources).

Additional Resources[1] Ettus Research, Universal Software Radio Peripheral (USRP), <https://www.ettus.com/product/details/UN210-KIT> (accessed July 2014)

[2] Galileo Open Service Signal in Space Inter-face Control Document (OS SIS ICD), issue 1.1, <http://ec.europa.eu/enterprise/policies/sat-nav/galileo/files/galileo-os-sis-icd-issue1-revision1_en.pdf> (accessed August 2013)

[3] Gunawardena, S. (2007), “Development of a Transform-Domain Instrumentation Global Positioning System Receiver for Signal Quality and Anomalous Event Monitoring.” Electronic Dissertation, Ohio University, 2007 <https://etd.ohiolink.edu> (accessed August 2013)

[4] Gunawardena, S. (2013), “A Universal GNSS Software Receiver MATLAB Toolbox for Education and Research,” Proceedings of the 26th Interna-tional Technical Meeting of The Satellite Divi-sion of the Institute of Navigation (ION GNSS+ 2013), pp. 1560-1576, Nashville, Tennessee, USA, September 2013

[5] Gunawardena, S. (2011), and F. van Graas, “Multi-Channel Wideband GPS Anomalous Event Monitor,” Proceedings of the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2011), pp. 1957–1968, Portland, Oregon, USA, September 2011

[6] Gunawardena, S. (2012a), F. van Graas, “High Fidelity Chip Shape Analysis of GNSS Signals using a Wideband Software Receiver,” Proceed-ings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navi-gation (ION GNSS 2012), pp. 874-883, Nashville, Tennessee, USA, September 2012

[7] Gunawardena, S. (2012b), and F. van Graas, “Analysis of GPS Pseudorange Natural Biases using a Software Receiver,” Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, Tennessee, USA, September 2012

[8] Gunawardena, S. (2014), and F. van Graas, “Analysis of GPS-SPS Inter-PRN Pseudorange Biases due to Receiver Front-End Components,” Proceedings of the 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, USA, September 2014

[9] Humphreys, T. E., and B. M. Ledvina, M. L. Psiaki, B. W. O’Hanlon, and P. M. Kintner, Jr.,

“Assessing the Spoofing Threat: Development of a Portable GPS Civilian Spoofer,” Proceedings of the 21st International Technical Meeting of the Satellite Division of The Institute of Naviga-tion (ION GNSS 2008), Savannah, Georgia, USA, September 2008, pp. 2314-2325

[10] Loctronix Corporation, ASR-2300 MIMO SDR, <http://www.loctronix.com/en/products_asr_2300.html> (accessed July 2014)

[11] MathWorks Inc., MATLAB: the language of technical computing<http://www.mathworks.com/products/matlab> (accessed August 2013)

[12] Mathworks Inc., “Introducing MEX-Files,”<http://www.mathworks.com/help/mat-lab/matlab_external/introducing-mex-files.html> (accessed July 2014)

[13] Nuand, bladeRF Software Defined Radio, <http://nuand.com> (accessed June 2014)

[14] Ouvry, L., and C. Boulanger and J. R. Lequepeys, “Quantization effects on a DS-CDMA signal,” Spread Spectrum Techniques and Appli-cations, 1998, Proceedings of the 1998 IEEE 5th International Symposium, vol.1, pp. 234,238 vol.1, 2-4 September 2–4, 1998

[15] Sparkfun Electronics, SiGe GN3S Sam-pler v3, <https://www.sparkfun.com/prod-ucts/10981> (accessed August 2013)

AuthorSanjeev Gunawardena is a GNSS research and development engineer with more than 15 years of professional experi-ence in the field. He received his Ph.D. in

electrical engineering from Ohio University. His research interests include RF design, digital sys-tems design, high performance computing, soft-ware radio, and all aspects of GNSS receivers and associated signal processing.


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http://www.ChameleonChips.com

https://www.ettus.com/product/details/un210-kit

http://ec.europa.eu/enterprise/policies/satnav/galileo/files/galileo-os-sis-icd-issue1-revision1_en.pdf

http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1178558967

http://www.loctronix.com/en/products_asr_2300.html

http://www.mathworks.com/help/matlab/matlab_external/introducing-mex-files.html

http://nuand.com

https://www.sparkfun.com/products/10981

http://www.mathworks.com/products/matlab






In GNSS receivers, the acquisition process is the first stage of the sig-nal-processing module. It consists in

assessing the presence of GNSS signals and providing a rough estimation of the incoming signal parameters: the Doppler frequency and the code delay.

To detect the presence of the signal, the received signal is correlated with a succession of locally generated replicas until the acquisition detector crosses a predefined threshold. One commonly used criterion of acquisition perfor-mance is the probability of detection when the parameters of the local replica are (close to being) correct. This prob-ability should be as high as possible but under unfavorable conditions, such as adverse environments, detection becomes a challenge.

Initially, GNSS signals were only defined on one component (such as GPS L1 C/A) but the new generation of sig-nals has two components (such as GPS L1C, GPS L5, Galileo E1 OS, Galileo E5 a/b, and so forth): a data component that carries the navigation message and a pilot component, which does not carry any useful information.

Designers of the modern civil signals introduced the pilot component in order to avoid the data bit transition prob-

lem during the tracking process. From the point of view of signal acquisition, however, the presence of a systemati-cally known secondary code on the pilot component still implies bit sign transi-tion. The presence of the pilot signal also means that the total signal power is split between components, thus impacting the way to process such a signal to gather all the signal power.

The objective of this article is to study the typical sources of performance deg-radations of the GNSS acquisition pro-cess that are generally overlooked in the literature and to assess their effects on the acquisition of new GNSS civil sig-nals. We will focus on degradations due to (1) the uncertainties brought by the choice of the acquisition grid, (2) the presence of bit sign transition, and (3) the non-compensation of the code Dop-pler. Further to the pure acquisition per-formance, we also analyze the acquisi-tion of the secondary code for new GNSS signals and the frequency refinement because these factors are necessary con-ditions with which to initiate standard tracking.

This study takes place in the context of the development of a GNSS software receiver that aims at acquiring any GNSS civil signals at 27 dB-Hz and higher with

© iStockphoto.com/ albln

The low power and spread spectrum nature of GNSS signals make their detection and acquisition a key, but challenging aspect of receiver processing designs. A team of researchers investigated the performance of four new GNSS signals and the legacy GPS L1 C/A code, comparing their probability of detection at a specific level of received signal strength. Factors of particular interest included the bit sign transition, acquisition bin size, and uncompensated code Doppler.

MYRIAM FOUCRAS, BERTRAND EKAMBI, FAYAZ BACARDABBIA GNSS TECHNOLOGIESOLIVIER JULIEN, CHRISTOPHE MACABIAUÉCOLE NATIONALE DE L’AVIATION CIVILE (ENAC)

WORKING PAPERS

Assessing the Performance of GNSS Signal AcquisitionNew Signals and GPS L1 C/A Code


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a strong probability of detection set to 95 percent. As a consequence, all presented results refer to this test case.

In this article, we will first introduce the required acquisition parameters to achieve the 27 dB-Hz/95 percent objec-tive without considering any aforemen-tioned source of degradation. Then, we discuss each point of degradation inde-pendently and analyze its effect on the probability of detection.

GNSS SignalsIn this article, we consider the civil GPS and Galileo signals in the L1/E1 and L5/E5 bands.

The main points of design of a GNSS signal are:

fLc1 characterized

by their length Nc1, its chipping rate fc1, or equivalently its chip duration Tc1 = 1/fc1

d on the data component and the secondary code c2 on the pilot component (and some-

times also on the data component). Table 1 summarizes the main signal

features for the considered GNSS sig-nals.

The down-converted and filtered composite GNSS signal entering the correlation block of the receiver can be generically represented as follows:

wherex stands for “D” for the data compo-nent and “P” for the pilot componentAx is the signal amplitude on the component and depends upon the total signal power Cpx is the subcarrier modulating the spreading codes τ is the receiver PRN code delay f1F is the received intermediate fre-quency of the receiverfd is the incoming Doppler frequencyφ0,x is the initial phase on each com-ponent depending on the initial phase of the incoming signaln is the incoming noise, which is

assumed to be a white noise with centered Gaussian distribution and a constant two-sided power spectral density equal to N0/2 dBW-Hz.Note that in this expression, the role

of the RF front-end equivalent filter is purposely ignored for simplification reasons.

To complete the generic expression of the received GNSS signal (1), Table 2provides the value for each parameter. As can be seen, the GNSS L5 signals are in quadrature; however, the phase rela-tionship between the two components of GPS L1C is not yet specified. (For details, see the article by J. W. Betz et alia listed in Additional Resources section near the end of this article.)

For the purposes of this article, we designated L1C to be an in-phase signal as is the case for Galileo E1 OS. GPS L1C presents a power difference in both com-ponents — 75 percent of the power in the pilot component and 25 percent of power

fL (MHz) Modulation

Spreading code Data Secondary codeLength Tc1 (ms)

Nc1 (chips)

Rate wrt MHz

Symbol duration Td (ms)

Code length Tc2

(ms) Nc2

(bits)Bit duration

(ms)

GPS L1 C/A 1575.42 BPSK 11023 f0 20 None None

GPS L1CData 1575.42 BOC(1,1) 10

10230 f0 10 None None

Pilot 1575.42 TMBOC(6,1,1/11) 1010230 f0 None 18 000

1800 10

GPS L5Data 1176.45 BPSK(10) 1

10230 10 × f0 10 1010 1

Pilot 1176.45 BPSK(10) 110230 10 × f0 None 20

20 1

Galileo E1 OS

Data 1575.42 CBOC(6,1,1/11,’+’) 44092 f0 4 None None

Pilot 1575.42 CBOC(6,1,1/11,’-‘) 44092 f0 None 100

25 4

Galileo E5aData 1176.45 BPSK(10) 1

10230 10 × f0 1 2020 1

Pilot 1176.45 BPSK(10) 110230 10 × f0 None 100

100 1

Galileo E5bData 1207.14 BPSK(10) 1

10230 10 × f0 1 44 1

Pilot 1207.14 BPSK(10) 110230 10 × f0 None 100

10 1

TABLE 1. Key feaatures of GNSS signals


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in the data component — whereas the total signal power is split in half 50/50 for the other GNSS composite signals.

GNSS Acquisition Performance in Ideal CaseThis section presents the acquisition process in the case when none of the sources of error mentioned in the intro-duction are considered which can be found in many assessment articles in the literature. As explained in the introduc-tion, the chosen test case is to acquire any GNSS civil signals at 27 dB-Hz (total signal carrier-to-noise-density ratio or C/N0) with a probability of detection of 95 percent.

Correlation Operation. Considering the correlation operation for one component of the GNSS signal and assuming that:

during the correlation processTI

seconds-

nal and the local replica are constant during the correlation operation such that the code delay error ετ and the Doppler frequency error εf are con-

stant and the carrier phase error at the beginning of the correlation pro-cess is εΦ0.

The in-phase and quadrature-phase cor-relator outputs can be modelled as:

wherenx,I and nx,Q are the noises at the cor-relator output (independent) that fol-low a centered Gaussian distribution with variances Rx is the autocorrelation function on the x component of the signal.Note that the aforementioned corre-

lator outputs model neglects the cross-correlation between the data and pilot component because the spreading codes were chosen to be as orthogonal as pos-sible. Note also that the local spread-ing code is assumed to have the same modulation as the spreading code of the received signal.

Acquisition detector. A receiver can acquire composite GNSS signals by using correlator outputs based on one of the two components (in general, the

pilot component) or both data and pilot components. In either case, the acqui-sition detector is defined as the sum of the squared correlator outputs (2 when only one component is used, 4 when two

components are used). The acquisition detector for one component is thus

where K represents the number of non-coherent summations. In this case KTI is referred to as dwell time, and the param-eters of the local replica (local PRN code delay and Doppler ) are constant for the K correlations.

The acquisition detector based on the use of two components can be easily derived accordingly.

Probability of detection. The basic principle of acquisition is to sequentially compute the acquisition detector for all possible values of local code delay and local Doppler until the detector crosses a predefined threshold Th. The set of the tested couples ( , ) is defined as the

WORKING PAPERS

Data component Pilot componentAD c2,D pD ϕ0,D Ap Pp ϕ0,P

GPS L1 C/A 1 1 ϕ0 0 None None

GPS L1C

25%1 pBOC(1)

(t) ϕ0 None ϕ0

GPS L550%

NH10 1 ϕ0 50%1 ϕ0

Galileo E1 OS50%

1 ϕ0 50%ϕ0

Galileo E5a50%

1 1 ϕ0 50%1 ϕ0

Galileo E5b50%

1 1 ϕ0 50%1 ϕ0

with

where pBOC(y)(t) = sign(sin(2π × y × f0t))

TABLE 2. GNSS signals features


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acquisition matrix, and its size depends upon the uncertainty on the incoming signal code delay and Doppler frequency and on the sampling of these uncertain-ties.

The tested values of the acquisition matrix are referred to as acquisition bins, and the distance between two consecutive tested values is referred to as bin size. The detection performance of such a detector is generally computed based on a hypothesis test for each vis-ited acquisition matrix bin: hypothesis H0 assumes that the desired signal is not present and is tested against hypothesis H1 that assumes that it is present.

Under hypothesis H0 the correlator outputs only consist of independent Gaussian noises. In this case, the (nor-malized) detector follows a centered χ2

distribution with 2K or 4K degrees of freedom for the one-component and two-component cases, respectively. For a desired probability of false alarm Pfa, we can thus define the appropriate threshold Th.

The a lternative hypothesis H1assumes that the signal is present, mean-ing that the parameters of the local rep-lica are almost aligned with the ones of the received signal. In this case, the (nor-malized) detector follows a non-central χ2 distribution with 2K or 4K degrees of freedom for one-component and two-component cases, respectively.

The non-central parameter of the χ2

distribution depends upon the receiver signal C/N0, the correlation duration TI,and the uncertainty of the parameters ( , ) due to the acquisition bin size. We can then compute the probability of detection Pd by comparing the detec-tor distribution to the threshold Th. As a synthesis, the key acquisition param-eters are presented in Table 3.

Minimum Dwell Time to Reach a Desired Probability of Detection. To find results related to our test case, we selected a desired probability of false alarm Pfa = 1e–3 as described in the RTCA, Inc. arti-cle referenced in Additional Resources. To reach this objective, determining the dwell time K×TI is important. As TI is generally taken equal to the spreading code period during the acquisition pro-cess, K is the critical parameter to play with.

Assuming that the acquisition bin size is infinitely small (thus meaning that ετ = εf = 0), Table 4 indicates the value of K to reach the proposed objec-tive. This table shows that the composite GNSS signals having a data/pilot power share of 50/50 percent require a dwell time twice as short when both compo-nents are used compared to when only one component is used.

In the table, note that for GPS L1C, with a data/pilot power share of 25/75 percent, using only the pilot component or both components produces equivalent results. Finally, the well-known prefer-

ability of having a long coherent inte-gration time to improve the acquisition detection performance explains why, for example, the GPS L1C and Galileo E1 OS require a lower dwell time than GPS L1 C/A or GPS L5. (See the discussion in F. Bastide et alia cited in Additional Resources.)

Effect of Acquisition Bin Size on Acquisition Detection PerformanceClearly, it is irrelevant to assume that the acquisition bin size is infinitely small. Indeed, a trade-off should be chosen between the acquisition bin size and the acquisition duration: a large bin size leads to degradation of the acquisition performance (the error between the test-ed values and the true values can be sig-nificant), while a narrow bin size means a significant number of bins potentially have to be visited, thus increasing the mean-time-to-acquire the signal.

In general, the acquisition grid is defined as a function of the maximum acceptable degradation on the detec-tor. Following the example used in the RTCA/DO-235B, we chose

TI, corre-sponding to an equivalent degrada-tion of the received signal C/N0 of 0.9 dB, which corresponds to a maxi-mum Doppler frequency error |εf | ≤ 1/4TI

sufficient to generate a maximum equiva lent degradation of the received signal C/N0 of 2.5 decibels. The code delay bin size thus depends on the autocorrelation function shape (and in fact on the RF front-end filter as well). For example, it corresponds to a bin size of one-half chip for an unfiltered GPS L1 C/A or GPS L5 sig-nal.Figure 1 shows the probability of

detection as a function of the Doppler

Pilot component acquisition Total signal acquisitionAcquisition detector T = TP T = TD + TP

Threshold

Probability of detection

Non-centrality parameter

where Fχ2(ddl) is the approximately equal cumulative distribution function of a χ2 distribution with ddl degrees of freedom.

TABLE 3. Acquisition as a detection problem

GPS L1 C/AGPS L1C GPS L5 Galileo E1 OS Galileo E5a and E5b

Pilot Both Pilot Both Pilot Both Pilot BothK 126 6 5 433 217 40 20 433 217

Dwell time KT1 (ms) 126 60 50 433 217 160 80 433 217

TABLE 4. Required dwell time to acquire signal with a C/N0 or 27 dB-Hz for a desired probability of detection of 95%


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uncertainty created by the bin size for the selected test case and the number of non-coherent summations given by Table 4. In the worst case (limit of the cell), the probability of detection falls from 0.95 down to 0.8. A more rel-evant figure is the average probability of detection over the bin, assuming that the actual Doppler error is a random variable uniformly distributed over the entire bin. The average probability of detection is also plotted in Figure 1 and equals 0.91.

Figure 2 shows the same thing for the code delay uncertainty within one acquisition bin. In the worst location (on the edge of a bin in the acquisition grid), it goes down from 0.95 to 0.43 while the average probability of detection over the bin is 0.74.

If the worst cases in the frequency and time domains are combined, the total loss on the equivalent received C/N0 is 3.4 decibels and results in a prob-ability of detection down to 0.25 instead of 0.95. The average probability of detec-tion over the bin is around 0.67, thus showing a theoretical degradation of performance of 28 percent.

Bit Sign Transitions and Receiver PerformanceThe presence of bit sign transitions affects receiver performance in signal acquisition detection. The following discussion addresses this phenomenon and associated factors.

Bit Transition Problem. The correlator output models provided in Equation (3) assumed that the data and/or the sec-ondary code bits are constant during the correlation interval. During the acquisition process, however, we have no reason to assume that the integra-tion interval is aligned with the data bit. Although often neglected in the litera-ture, it thus seems necessary to develop the correlation output model consider-ing bit sign transitions. The authors have performed such a study, including the theoretical aspects for single- and dual-component signals, and a paper — M. Foucras et alia (2014a) in Additional Resources — describing the results will be submitted for publication. The fol-lowing is a short summary with corre-sponding results.

The presence of a bit sign transi-tion during the correlation operation degrades the useful part of the correlator output without modifying the power of the noise. This results in a degradation of the acquisition detector amplitude, the nature of which will depend upon the location of the bit sign transition in the integration interval, the number of non-coherent summations, and the Doppler frequency error εf as described in the paper by C. O’Driscoll. In particu-lar, the expression of the non-centrality parameter in case of a bit sign transition during the integration interval is given in M. Foucras et alia (2014a). As might be expected, the worst case is for a bit

sign transition occurring in the middle of the correlation interval.

For all GNSS signals discussed in this article except GPS L1 C/A, a bit sign transition can occur at each spreading code period. This means that the corre-lation duration should be limited to the code duration, and that even then, a bit sign transition can potentially degrade all correlator outputs. In the article by M. Foucras et alia (2014b), the authors have identified for each GNSS signal the resulting average probability of detec-tion for the number of bit sign transi-tions, taking into account the probabil-ity of occurrence.

In contrast, the acquisition perfor-mance of the GPS L1 C/A signal, when considering bit sign transition, depends on the correlation duration. Indeed, because the data bit duration is 20 times longer than the spreading code period, we can use correlation durations of 1, 2, 4, 5, 10 or 20 milliseconds. Each case will have a different probability of undergo-ing a sign transition during the corre-lation. Consequently, for an equivalent dwell time — say, 20 milliseconds — the effect on the acquisition performance depends on the choice of TI as explained in M. Foucras et alia (2014b).

As shown in Figure 3, when the TIis too short, the effect of the bit sign transition is slight, but it does not allow optimal detection. On the contrary, for long TI, the effect of the bit sign transi-tion is significant. Based on Figure 3, it

WORKING PAPERS

FIGURE 2 Probability of detection versus the code delay uncertainty for BPSK-modulated signals

Max. degradation of 2.5 dB code delay uncertainty : 0.43164)1

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0.4

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0

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abilit

y of d

etec

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–0.2 –0.1 0 0.1 0.2

Code delay error εf (chip)

Pd

Mean probability : 0.73585

Pd = 0.95

FIGURE 1 Probability of detection versus the Doppler frequency uncertainty

Max. degradation of 0.9 dB Doppler frequency uncertainty : 0.80026)1

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0

Prob

abilit

y of d

etec

tion

–200 –100 0 100 200

Doppler frequency error εf (Hz)

Pd

Mean probability : 0.90907

Pd = 0.95


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appears that a correlation duration of 4 to 10 milliseconds is optimal to have the lowest dwell time to reach a probability of detection of 95 percent.

Resulting Probability of Detection.Table 4 provided the required dwell time to reach a probability of detection of 95 percent for a signal with a C/N0 of 27 dB-Hz without considering bit sign transition or uncertainty due to the acquisition bin size. For the same dwell time and C/N0, Table 5 shows the aver-

age probability of detection based on Monte Carlo simu-lations assuming that the distribution of the location of bit sign transition is uniform within the correlation inter-val (assumed equal to one spreading code). As discussed previously, for GPS L1 C/A we chose the coherent inte-gration time to be the spreading code period (one milli-second). Note that in this latter case — considering the bit sign transition, Fig-ure 3 showed that this value for TI is not the optimal one.

Table 5 shows that all the compos-ite signals are highly affected, mostly due

to the fact that the correlation duration has to be chosen equal to the data bit/secondary code bit duration. As a con-sequence, it seems necessary for these signals to use techniques that are insen-sitive to data bit sign transitions, such as the techniques described in the article by M. Foucras et alia (2012). These tech-niques are generally more demanding in terms of resources. However, GPS L1 C/A is almost not affected thanks to its structure based on a data bit duration

20 times longer than the spreading code duration.

Uncompensated Code Doppler and Receiver PerformanceWe now turn to the question of the effect of an uncompensated code Doppler on acquisition detection performance.

Code Doppler problemThe Doppler frequency, mainly caused by the satellite motion and the receiver local oscillator, affects the processed sig-nal by modifying

change estimated by the acquisition process

resulting in a code Doppler fcd which depends on the incoming Doppler frequency fd, the carrier frequency fL and the chipping rate frequency fc1according to

The modification of the code fre-quency leads to a change in the spread-ing code period as can be seen in Figure 4 where three periods of a four-chip spreading code are represented:

causes the spreading code duration to shrink (Tcd < Tc1).

spreading code duration to expand (Tcd > Tc1).The problem of the presence of an

uncompensated code Doppler resulting in a difference between the code fre-quency of the received and the local sig-nals for GPS L1 C/A has been addressed by several authors. E. D. Kaplan and C. Hegarty. (See Additional Resources). Foucras et alia (2014c) showed that the degradations due to uncompensated code Doppler are even more significant for the new generation of GNSS signals

FIGURE 3 Average probability of detection at 27 dB-Hz for GPS L1 C/A

Average probability of detection (on t0)1

0.8

0.6

0.4

0.2

0

Prob

abilit

y of d

etec

tion

20 40 60 80 100

Total integration time KTI (ms)

TI = 1 ms

TI = 2 ms

TI = 4 ms

TI = 5 ms

TI = 10ms

TI = 20ms

GPS L1 GPS L1C GPS L5 Galileo E1 OS Galileo E5a and E5bC/A Pilot Both Pilot Both Pilot Both Pilot Both0.94 0.71 0.67 0.56 0.56 0.62 0.62 0.56 0.56

TABLE 5. Probability of detection when considering bit sign transitions for a C/N0 of 27 dB-Hz

GPS L1 C/A (dwell Time =

126 ms)

GPS L1C (dwell Time =

50 ms)

GPS L5 (dwell Time =

217 ms)

Galileo E1 OS (dwell Time =

80 ms)

Galileo E5a (dwell Time =

217 ms)

Galileo E5b (dwell Time =

217 ms)Incoming Doppler frequency

1 kHz 0.081 0.033 1.887 0.052 1.887 1.8395 kHz 0.409 0.162 9.435 0.260 9.435 9.19510 kHz 0.818 0.325 18.870 0.520 18.870 18.390

TABLE 6. Offset between the local and received spreading code after the dwell time (in chips)

FIGURE 4 Code Doppler effect on the spreading code period

1st period 2nd period 3rd period

1st period 2nd period 3rd period

Local code

Received code


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(higher code frequency, lower L-band central frequency, BOC modulation).

Table 6 presents the offsets between the local and received spreading codes after the dwell time for the signals being con-sidered in this article. For GPS L1 C/A, GPS L1C, and Galileo E1 OS, the offset is lower than one chip even for high incoming Doppler frequency. Still, the offset can sometimes be greater than one code delay bin size, which can be problematic. For L5 signals, the offset exceeds one chip for an incoming Doppler of several hundreds of hertz with the considered dwell time. For high Doppler frequencies this means that the offset is too high to provide correct acquisition performance, as it will be shown later.

To illustrate this point, Figure 5, Figure 6, Figure 7 and Figure 8 represent the deformation of the squared correlation func-tions between the incoming signal spreading code and the local replica spreading code for GPS L1 C/A, GPS L5, Galileo

E1 OS and GPS L1C, respectively, due to uncompensated code Doppler and with the dwell times as defined in Table 4. For BPSK-modulated signals (GPS L1 C/A and GPS L5), the shape of the autocorrelation function becomes rounded and offset compared to the reference triangular curve. The amplitude of the maximum value of the correlation function is also reduced, and the peak is shifted to the right for a negative Doppler. The result is a degradation of the probability of detection and a potential missed detection due to the motion of the correlation peak with time.

Even if the correlation function–peak offset is not such a problem for GPS L1 C/A due to its relatively slow chipping rate, this can be a real problem for GPS L5, as seen in Figure 6 where the correlation peak has moved by more than one chip over the 217-millisecond dwell time. For Galileo E1 OS, the CBOC modulation’s correlation function has a significantly reduced amplitude and its shape becomes flat when the code Doppler

WORKING PAPERS

FIGURE 5 Autocorrelation function when considering code Doppler for GPS L1 C/A on 126 ms

GPS L1 C/A1

0.8

0.6

0.4

0.2

0

Squa

red a

utoc

orre

lation

func

tion

0.50-0.5-1 1 1.5 2

Code delay (chip)

0kHz–2kHz–4kHz–6kHz–8kHz–10kHz

FIGURE 6 Autocorrelation function when considering code Doppler for GPS L5 on 217 ms

GPS L51

0.8

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0

Squa

red a

utoc

orre

lation

func

tion

0.50-0.5-1 1 1.5 2

Code delay (chip)

0kHz

–2kHz

–4kHz

–6kHz

–8kHz

–10kHz

FIGURE 7 Autocorrelation function when considering code Doppler for Galileo E1 OS on 80 ms

Galileo E1 OS1

0.8

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0.4

0.2

0

Squa

red a

utoc

orre

lation

func

tion

0.50-0.5-1 1 1.5 2

Code delay (chip)


FIGURE 8 Autocorrelation function when considering code Doppler for GPS L1C on 50 ms

GPS L1C1

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0

Squa

red a

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func

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Code delay (chip)



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increases due to the presence of the side peaks. This can then cre-ate a detection problem as several bins could trigger a detection.

If the slip between the received and the local spreading codes exceeds one chip, then the correlation process no longer makes sense because the power of the signal cannot be accu-mulated since the correlator output is essentially noise. Figure 9 shows the linear relationship between the incoming Doppler frequency and the time to the slip of one chip.

For the maximum incoming Doppler frequency considered in this article (10 kilohertz), the slip of one chip occurs after 154 milliseconds for a GNSS signal at L1 and after only 12 mil-liseconds for GNSS L5 signals. For GPS L5, for example, that means the previously computed dwell time of 217 milliseconds would not be realistic as it implies a slip of 18 chips. So, the code Doppler clearly needs to be dealt with in a GPS L5 or Galileo E5a/E5b receiver, and potentially in a GPS L1 C/A, GPS L1C, or Galileo E1 OS receiver.

To complete this part of our investigation, Figure 10 presents the losses on the maximum amplitude of the squared autocor-

relation function. The maximum losses are for L5/E5 signals, because these experience a slip of more than one chip (Fig-ure 6). The minimum loss is for GPS L1 C/A (1.9 decibel for a code Doppler of 10 kilohertz), which is better than GPS L1C (2.5 decibels) and Galileo E1 OS (4.5 decibels) due to its BPSK modulation, even if the dwell time is longer (126 milliseconds instead of 50 or 80 milliseconds).

Resulting probability of detection. Let us now consider the resulting probability of detection taken in = 0 (Figure 11). Clearly, for GNSS L5 signals, the probability of detection decreases because the shift between the incoming and the local signals is too large.

Performance of the Acquisition-to-Tracking TransitionOnce acquisition has been successful, the frequency estimate is on the order of a few tens or hundreds of hertz, depending upon the acquisition bin size. However, at the initiation of the track-ing process, a refinement on the Doppler frequency is required in order to ensure locking the phase lock loop (PLL).

Frequency tracking. One solution is to use a frequency lock loop (FLL), which refines the estimation of the Doppler frequency. This is a critical stage in GNSS signal processing because, if this transition is not well calibrated, even a success-ful acquisition can lead to unsuccessful tracking, especially at low received C/N0.

The authors undertook a performance study for various FLL schemes, which was described in the article by M. Fou-cras et alia (2014d) listed in Additional Resources. Based on the proposed test case, the probability of achieving FLL lock was analyzed assuming a C/N0 of 27 dB-Hz. The four FLL dis-criminators examined in the study are the cross-product (CP), the decision directed cross product (DDCP), the differential arctangent (Atan), and the four-quadrant arctangent (Atan2). We should mention that during this initial phase of GNSS sig-nal tracking being studied, bit synchronization has not yet been achieved.

FIGURE 9 Time for the slip of one chip in function of the incoming Doppler frequency

2000

1500

1000

500

0

Slip o

f 1 ch

ip (m

s)

0 2 4 6 8 10

Incoming Doppler frequency (kHz)

GPS L1 C/A

GPS L1C

GPS L5

Galileo E1 OS

Galileo E5a

Galileo E5b

FIGURE 10 Losses on the autocorrelation function due to code Doppler

0

-5

-10

Loss

in m

ax of

auto

corre

lation

func

tion (

dB)

4 6 8 10

Doppler (kHz)

GPS L1 C/AGPS L1CGPS L5Galileo E1 OSGalileo E5aGalileo E5b

FIGURE 11 Probability of detection considering the total signal power and code Doppler

1

0.8

0.6

0.4

0.2

0

Prob

abilit

y of d

etec

tion

0 2 4 6 8 10Incoming Doppler frequency (kHz)

GPS L1 C/AGPS L1CGPS L5Galileo E1 OSGalileo E5aGalileo E5b


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WORKING PAPERS

Table 7 summarizes the expressions, linear regions, and characteristics of the four candidate FLL discriminators. As only two discriminators are bit sign–transition insensitive, this feature plays a key role in the choice of the discrimi-nator for the best FLL scheme.

Probability of Successful Transition.The key figure of merit for the acqui-sition-to-tracking process is the prob-ability of successful transition (or con-vergence) of the FLL, regardless of the initial frequency error after acquisition (within the correct acquisition bin, thus with a Doppler error within

hertz in the proposed case) as a func-tion of the GNSS signal and the FLL dis-criminator. The convergence is assessed

by making sure that the loop is locked after 20 seconds of tracking. The prob-abilities are obtained based on 200 runs per configuration.

For the simulations, the article by M. Foucras et alia (2014d) (Additional Resources) showed that it is better to choose an FLL loop bandwidth BL that is relatively reduced even though this reduces the response time of the loop. BL = 1 Hz is used in the following results.

Finally, for composite GNSS signals, two techniques were investigated: the first one consists of tracking only the pilot component and the second one consists of tracking both components by computing a FLL discriminator based on an average of the data and pilot dis-criminators (thus using the whole avail-able signal power).

Two figures present the probabilities of successful transition for a signal with a C/N0 equal to 27 dB-Hz. Figure 12 con-siders the pilot-only cases whereas Fig-ure 13 considers a scheme using the total available power. As expected, successful convergence depends upon the initial frequency error (it is better to start close to the correct value).

In the legend of each figure, the mean probability of successful transition in the cell is provided. As can be observed, for GPS L1 C/A, whatever the Doppler initial frequency error, the FLL always converges using the CP or Atan2 dis-criminators, thus finely dealing with bit sign transitions.

-ever, this is no longer the case:

Discriminator expression Linear region Characteristics

CP Linear region independent from SNR

DDCP Bit transition insensitive

Atan Bit transition insensitive

Atan2 Highest linear region

where U is the phase unwrapping function which maps the phase estimate to the interval and the Cross(k) and Dot(k) expressions are defined by

TABLE 7. Frequency discriminators

FIGURE 12 FLL schemes results when using only pilot component

1

0.5

0

P I

0-10-20 10 20

GPS L1C

Frequency (Hz)

DDCP : 0.93059Atan : 0.94784

DDCP : 0.014706Atan : 0.11941

1

0.5

0

P I

0-10-20 10 20

Galileo E5

Frequency (Hz)

1

0.5

0

P I

0-50 50

Galileo E1 OS

Frequency (Hz)

DDCP : 0.93059Atan : 0.94784

FIGURE 13 FLL schemes results when using total signal power

1

0.5

0

P I

0-10-20 10 20

GPS L1C

Frequency (Hz)

DDCP : 0.95412Atan : 0.954331

1

0.5

0

P I

0-100-200 100 200

GPS L1 CA

Frequency (Hz)

CP : 1Atan2 : 1

DDCP : 0.031176Atan : 0.22529

1

0.5

0

P I

0-10-20 10 20

Galileo E5

Frequency (Hz)

1

0.5

0

P I

0-50 50

Galileo E1 OS

Frequency (Hz)

DDCP : 0.93765Atan : 0.9051


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presence of 75 percent of the signal power contained in the pilot compo-nent, the difference between the two schemes (considering pilot or both components) is slight. The perfor-mance for both bit transition–insen-sitive discriminators is similar and around 0.95 in mean value.For Galileo composite signals, it

appears preferable to use both com-ponents. In this case, for the Galileo E1 OS signal, the average value is 0.94 for the DDCP discriminator and has a performance very similar to GPS L1C. For the Galileo E5a or Galileo E5b sig-nals (and GPS L5, not shown here), the probabilities to get locked are very low (mean value 0.23), which constitutes a significant problem for the acquisition-to-tracking transition. This can be explained by the short integration time (one millisecond) associated to these signals which implies a high correlator output noise variance for a signal with a C/N0 equal to only 27 dB-Hz.

Secondary code acquisition perfor-mance. The pilot component was initially introduced to avoid the data bit transi-tion problem on the data component. Indeed, the pilot component is free of transition once the secondary code is demodulated. This leads to the use of longer coherent integration for a more robust tracking.

In an article by M. Foucras et alia(2013), the authors provided a detailed analysis on the probability of acquiring the secondary code for several GNSS composite signals. The main conclusion of this study was that the C/N0 threshold to acquire the secondary code with a very high probability was much lower than 27 dB-Hz and should not be a problem.

ConclusionsSignal acquisition is a crucial processing step in GNSS receivers. A useful signal must be extracted from the incoming signal that is assimilated in the back-ground RF noise, and its parameters should be estimated. Due to these con-ditions, the acquisition process at low received C/N0 is a challenge.

We conducted a detailed analysis of all the sources of acquisition degrada-tions, treating each point separately as

described in this article, to understand its specific effect. Our emphasis was on the probability of detection, voluntarily putting aside the time-to-acquire fac-tor, which is operationally of equivalent importance. The article also concentrat-ed on a specific test case, which was to be able to acquire a GNSS signal with a received C/N0 of 27 dB-Hz with a prob-ability of detection of 95 percent.

The first point that we addressed was the degradation of signal acquisition performance caused by the estimated parameters’ uncertainty brought by the size of the acquisition bin. A typical bin size results in an average degradation of the probability of detection on the order of 5 to 20 percent in the test case that we considered.

We then showed that the problem of the bit-sign transition was not a big issue for the acquisition of GPS L1 C/A. This is because a data bit transition can occur only every 20 spreading code periods, and a good choice of the coher-ent integration time enables a receiver to limit the degradation of acquisition performance.

However, for the new GNSS signals considered in our research, a bit sign (data or secondary code) transition can occur at each spreading code period, and the adverse effect on the acquisition per-formance can become substantial. As a consequence, we highly recommend use of a transition-insensitive acquisition technique for these signals even if they are more computationally expensive.

We also showed that an uncompen-sated code Doppler particularly affects the acquisition performance for GNSS L5 signals due to their high frequency chipping rate. If not taken care of prop-erly, this effect results in a correlation function shape becoming rounded and flattened, leading to a potentially poor estimation of the incoming code delay. Our research also showed that the BOC-based signals are more influenced by code Doppler due to the shape of their correlation function. As a consequence, if code Doppler is not taken into account by the receiver, it becomes necessary to limit the acquisition dwell time even if this penalizes the acquisition perfor-mance at low C/N0 (it does anyway).

Finally, we described the use of FLL for the carrier acquisition-to-tracking process, with the main conclusions being to use bit transition–insensitive discriminators for composite GNSS sig-nals.

Additional Resources[1] Bastide, F., and O. Julien, C. Macabiau, and B. Roturier, “Analysis of L5/E5 Acquisition, Tracking and Data Demodulation Thresholds,” in Proceed-ings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navi-gation (ION GPS 2002), Portland, Oregon, USA, 2002, pp. 2196 – 2207

[2] Betz, J. W., and M. A. Blanco, C. R. Cahn, P. A. Dafesh, C. J. Hegarty, K. W. Hudnut, V. Kasemsri, R. Keegan, K. Kovach, L. S. Lenahan, H. H. Ma, J. J. Rushanan, D. Sklar, T. A. Stansell, C. C. Wang, and S. K. Yi, “Description of the L1C Signal,” in Proceedings of the 19th International Technical Meeting of the Satellite Division of The Insti-tute of Navigation (ION GNSS 2006), Fort Worth, Texas, USA, 2006, pp. 2080 – 2091

[3] Curran, J. T., “Weak Signal Digital GNSS Track-ing Algorithms,” Ph.D. thesis, National University of Ireland, Cork, 2010

[4] Foucras, M., (2012) O. Julien, C. Macabiau, and B. Ekambi, “A Novel Computationally Effi-cient Galileo E1 OS Acquisition Method for GNSS Software Receiver,” in Proceedings of the 25th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2012), Nashville, TN, USA, 2012, pp. 365 – 383

[5]Foucras, M., (2013) and O. Julien, C. Maca-biau, and B. Ekambi, “Probability of Secondary Code Acquisition for Multi-Component GNSS Signals,” in Proceedings of the 6th European Workshop on GNSS Signals and Signal Process-ing (SIGNALS 2013), Neubiberg, Germany, 2013

[6] Foucras, M., (2014a) O. Julien, C. Macabiau, B. Ekambi, and F. Bacard, “Probability of Detec-tion for GNSS Signals with Sign Transitions,” IEEE Transactions in Aerospace Electronic Systems,submitted July 2014

[7] Foucras, M., (2014b) O. Julien, C. Macabiau, B. Ekambi, and F. Bacard, “Optimal GNSS Acqui-sition Parameters when Considering Bit Transi-tions,” in Proceedings of IEEE/ION PLANS 2014,Monterey, CA, USA, 2014

[8] Foucras, M., (2014c) O. Julien, C. Macabiau, and B. Ekambi, “Detailed Analysis of the Impact of the Code Doppler on the Acquisition Perfor-mance of New GNSS Signals,” in Proceedings of the 2014 International Technical Meeting of The Institute of Navigation, San Diego, CA, USA, 2014, pp. 513 – 524

WORKING PAPERS


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[9] Foucras, M., (2014d) and U. Ngayap, J. Y. Li, O. Julien, C. Macabiau, and B. Ekambi, “Performance Study of FLL Schemes for a Successful Acquisition-to-Tracking Transition,” in Proceedings of IEEE/ION PLANS 2014, Monterey, California, USA, 2014.[10] Jiao, X., and J. Wang, and X. Li, “High Sensi-tivity GPS Acquisition Algorithm Based on Code Doppler Compensation,” in IEEE 11th Interna-tional Conference on Signal Processing (ICSP),Beijing, China, 2012, pp. 241 – 245

[11] Kaplan, E. D., and C. Hegarty, Understanding GPS: Principles and Applications, 2nd edition.Artech House, 2005

[12] O’Driscoll, C., “Performance Analysis of the Parallel Acquisition of Weak GPS Signals,” Ph.D. thesis, National University of Ireland, 2007

[13] Parkinson, B. W., and J. J. Spilker, Global Positioning System: Theory and Applications, Progress in Astronautics and Aeronautics., Vol. I, 1996

[14] Psiaki, M. L., “Block Acquisition of Weak GPS Signals in a Software Receiver,” in Proceed-ings of the 14th International Technical Meet-ing of the Satellite Division of The Institute of Navigation (ION GPS 2001), Salt Lake City, UT, USA, 2001, pp. 2838 – 2850

[15] RTCA, Inc., “Assessment of Radio Frequency Interference Relevant to the GNSS L1 Frequency Band RTCA/DO-235B.” 13-Mar-2008

[16] Van Diggelen, F. S. T., A-GPS: Assisted GPS, GNSS, and SBAS, GNSS Technology and Applica-tions Series. Artech House, 2009

AuthorsMyriam Foucras received her master’s degrees in mathematical engineer-ing and fundamental mathematics from the University of Toulouse. Since 2011, she has

been a Ph.D. student at the Signal Processing and Navigation (SIGNAV) research group of the TELECOM laboratory of Ecole Nationale de l’Aviation Civile (ENAC). Funded by ABBIA GNSS Technologies, in Toulouse, France, her work con-sists in the development of a GPS/Galileo soft-ware receiver.

Olivier Julien is the head of the Signal Processing and Navigation (SIG-NAV) research group of the TELECOM laboratory of ENAC, in Toulouse, France. His research

interests are GNSS receiver design, GNSS mul-tipath and interference mitigation and GNSS interoperability. He received his engineering

degree in digital communications from ENAC and his Ph.D. from the Department of Geomatics Engineering of the University of Calgary, Canada

Christophe Macabiaugraduated as an elec-tronics engineer from the ENAC in Toulouse, France. Since 1994, he has been working on the application of satellite

navigation techniques to civil aviation. He received his Ph.D. and has been in charge of the TELECOM laboratory of the ENAC since 2011.

Bertrand Ekambi gradu-ated from the University of Toulouse with a mas-ter’s degree in mathe-matical engineering. Since 2000, he has been involved in the main

European GNSS projects: EGNOS and Galileo. He is the founder manager of ABBIA GNSS Tech-nologies, a French small/medium-sized enter-prise working of the space industry, based in Toulouse, France

Fayaz Bacard received his master of science in engineering, specializ-ing in electronics and computer engineering from Ecole Nationale Supérieure des Sciences

Appliquées et Technologies (ENSSAT) in Lanion. Since 2013, he has been a software engineer at ABBIA GNSS Technologies.

Prof.-Dr. Günter Heinserves as the editor of the Working Papers col-umn. He is the head of the EGNOS and GNSS Evolut ion Program Department of the Euro-

pean Space Agency. Previously, he was a full pro-fessor and director of the Institute of Geodesy and Navigation at the Universität der Bundeswehr München. In 2002, he received the Johannes Kepler Award from the U.S. Institute of Navigation (ION) for “sustained and significant contribu-tions” to satellite navigation. He is one of the inventors of the CBOC signal.

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Aseries of ABI Research studies released in recent months sketches

a promising future of robust growth in GNSS OEM markets. .

The London- and New York–based company’s 2014 GNSS IC vendor matrix released on June 30 concluded that Qualcomm is the leading GPS IC vendor, followed by Broadcom, and — for the first time — MediaTek in third

place after another year of strong growth and robust shipments as a result of its targeted design strategy.

ABI Research predicts that the GNSS IC market will break $2 billon by 2016, with a host of secondary markets start-ing to emerge. Senior analyst, Patrick Connolly says that the firm expects that consumer GNSS IC OEMs adopt-ing technologies the produce sub-meter

accuracy, such as u-blox’s take on precise point positioning (PPP) will open up markets around unmanned aerial vehi-cles (UAVs), machine control, timing and synchronization, advanced driver assistance systems (ADASs), driverless cars, and other applications. As a result, total GNSS IC revenues are forecast to hit $2.75 billion in 2019, he says.

u-blox’s PPP technology uses carrier phase tracking and ionospheric cor-rection data from satellite-based aug-mentation systems, such as WAAS and EGNOS, and has been implemented in the company’s single-frequency NEO-7P GNSS module.

INDUSTRY VIEW

Studies Predict 2019 GNSS OEM Market to Hit $2.75 Billion

360 Degrees – Communications Act

Continued from page 21.

receivers would have to meet as a way to limit interference and expand the number of potential users.

Standards could stifle innovation, wrote the Alliance and, in the case of GPS, where the receivers can be baseball sized or tiny enough to fit in a watch, engineering a solution for all the receivers may be impractical.

One issue the committee has raised, but the Alliance did not address, was changing the role of the National Telecom-munications and Information Administration (NTIA). CTIA, the association for the wireless industry suggested that the FCC be put completely in charge of spectrum decisions.

“CTIA recommends Congress consider changing NTIA’s role so that, consistent with national security concerns, spec-trum use decisions are all made by the FCC,” the association wrote in its comments. The NTIA would still play a crucial function as an “advisor” to federal agencies, CTIA suggested, and would be the organization that would request spectrum from the FCC for federal users.

Changing the role of the NTIA could leave the GPS com-munity with one less empowered potential advocate at the table. The organization played a key role in debate over Light-Squared and in the decision not to go forward despite strong support for the project in the White House and then-FCC Chairman Julius Genachowski.

Given the momentum behind the project at the time, it is not clear what would have happened if the FCC had been able to proceed without the moderating effect of having to work with another agency that was able to organize additional, independent technical experts.

Politics versus Technology“Political idealism is what got us into this mess in the first place, rather than taking a hardnosed engineering view,” said

Tim Farrar of TMF Associates, a consulting firm that closely follows mobile communications industry.

Politics of a different sort are likely to add momentum to the lawmakers’update effort.

Upton took over the committee chairmanship in 2011. Given the current six-year term-limit rules for House Repub-licans, this means he will have to give up his committee lead-ership during 2017. Walden does not appear to be in the best position to assume the chair — at least four other committee members have more seniority. Although the next chairman could be supportive, Upton and Walden will likely want to get as much done as possible before the handover.

And Walden has his own reasons to be proactive. He is not only the subcommittee chairman; he chairs the National Republican Congressional Committee (NRCC), a group dedi-cated to electing Republicans to the U.S. House. As a member of the Republican leadership, he has input into the House’s agenda and can be an advocate for overhauling the law. But as the head of the NRCC, he will also be paying attention to fundraising and political messaging. More commercial spec-trum is broadly touted as a way to generate jobs and taking action to support job growth is only smart during this election year and the next.

Also smart is tackling issues bound to capture the atten-tion — and resources — of well-heeled industries. According to the watchdog group MapLight, which monitors money in politics, the telephone utilities made more than $7.3 million in campaign contributions during 2012 and 2013 while the cellular systems and equipment firms dropped in more than $2 million and the cable and satellite TV production and dis-tribution wrote checks worth more than $7.7 million. Accord-ing to Oregonlive.com and MapLight, Walden received more than $109,000 from the cable companies, the highest amount in Congress from that group for the two-year period.


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As single-purpose portable naviga-tion device (PND) shipments continue to fall, GNSS OEMs can expect new opportunities in cycling, eyewear, golf, and personal tracking to help the over-all market to continue to grow. The total market for dedicated GPS devices in 2013 was estimated at 35 million units, which is expected to grow almost 30 percent over the forecast period, despite accelerated decline in Western PND markets.

ABI Vice-President and practice director Dominique Bonte adds, “From an automotive perspective, the connect-ed car, infotainment, insurance, telemat-ics/diagnostics, safety, will all increase adoption of GNSS. We can expect to see cars fitted with multiple GNSS ICs in the future.” Bonte says “strong signals” suggest that some GNSS systems, such as Galileo and GLONASS, will be man-dated for regional automotive applica-tions such as emergency calling.

ION GNSS+, IGMPartner on Show PubThe Institute of Navigation is partner-

ing with the publisher of Inside GNSSmagazine (IGM) to produce, publish, and distribute an expanded Show Daily pub-lication at this year’s ION GNSS+ 2014 event taking place September 9–12 in Tampa, Florida. The alliance will leverage the magazine’s many years of publishing expertise to advance the business interests of exhibitors and attendees.

Inside GNSS will expand the Show Daily offering to include richer prod-ucts and company news sections as well as insider previews on content tracks planned for this year’s conference. Inside GNSS will also produce a digital version that will be sent to more than 20,000 GNSS professionals around the world.

ION Executive Director Lisa Beaty comments, “ION GNSS exhibitors, advertisers and conference attendees will

have much to gain with the expanded format, reach and in-depth reportage of the new Show Daily. ION is committed to bridging the divide between science and business, academia and industry, and we believe that Inside GNSS, led by Glen Gibbons and Richard Fischer, can help us achieve that.”

Inside GNSS, which reaches 30,000 readers worldwide with its print edi-tion and thousands more with its digital version and twice-monthly GNSS SIG-NALS e-newsletter, is in its ninth year of publication. Before taking on expanded responsibility for this year’s production, Gibbons Media & Research LLC, pub-lisher of the magazine, provided editorial support for the ION show daily for the past eight years

Companies interested in having a presence in the ION GNSS+ Show Daily may contact Fischer by e-mail <[email protected]> or phone 1-732-741-1964 (office) or 1-609-240-1590 (mobile).

P R E C I S E T I M E A N D T I M E I N T E R V A L M E E T I N G

Exhibit booths available.

ION PTTI 2014 SESSION TOPICSAdvanced Atomic Frequency Standards Applications | Advanced Clocks | Enhancing Resilience of Timing and Critical

Infrastructure | GNSS Present and Future | Industrial, Commercial and Military PTTI Systems, Applications and Technologies | New Commercial Products for PTTI Systems | Precise Networking Timing Standards, Requirements and Applications |

PTTI Systems Calibration | Signals of Opportunity | Space PTTI Applications | Time and Frequency Laboratory Activities and Updates | Time Scales, Algorithms and Methods | Traditional and Alternate Time and Frequency Transfer Methods


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See additional listings atWWW.INSIDEGNSS.COM/EVENTS

ADVERTISERS INDEXCompany Page NumberAntcom 18CAST Navigation 11ComNav Technology Ltd. 51Forsberg 41GPS Networking 35Honeywell 25IFEN 13Intergeo 34ION GNSS+ 2014 47ION PTTI 2014 81KVH Industries 3LabSat 8Locata 29NavCom Inside Back CoverNavtechGPS 42, 77NovAtel, Inc. Back Cover, 23, 32–33 Rohde & Schwarz Inside Front CoverSensonor 16–17, 19Septentrio 43Spectracom 45Spirent 6–7Terrastar 21Thales 27Topcon 9Trimble 5, 39, 59

GNSS TIMELINE

SeptemberSEPTEMBER 1-3ARAB INSTITUTE OF NAVIGATION (AIN) MELAHA CONFERENCE 2014Alexandria, Egypt“Resilience Navigation” is the theme of the 2014 Arab Institute of Navigation Conference and Exhibition, MELAHA. <http://www.ainegypt.org/>

SEPTEMBER 8-9CGSIC MEETINGTampa, Florida USAThe 54th meeting of the Civil GPS Service Interface Committee features in-depth updates on GPS from USAF, DoT, Coast Guard, State Department, Homeland Security and others. It is free and open to the public. <gps.gov/cgsic/meetings/2014/>

SEPTEMBER 9-12ION GNSS+ 2014Tampa, Florida USAThe 27th international technical meeting of the Institute of Navigation features a plenary session with explorer Tristan Gooley, plus 5-minute lightning talks with GNSS leaders on what’s hot - what’s not. <ion.org/gnss/>

OctoberOCTOBER 7-9INTERGEO 2014Berlin, GermanyGeodesy, Geoinformation and Land Management trade fair plus conference. It attracts 16,000 visitors from 92 countries who work in the surveying, geoinformation, remote sensing and photogrammetry fields. The conference language is German. <intergeo.de/intergeo-en/>

OCTOBER 8ERA-GLONASS 2014Moscow, RussiaGovernment and industry event on current status and future plans for the State Accident Emergency Response System. Sponsored by GLONASS Union, a consortium of Russia’s navigation and information services companies. <http://congress-era-glonass.com/>

OCTOBER 9–11ASIA OCEANIA REGIONAL WORKSHOP ON GNSSPhuket, ThailandWorkshop on Multi-Asia GNSS (MGA) collaborative experiments using multiple GNSS constellations in the region. The main organizer is JAXA, the Japanese Space Agency. <multignss.asia/workshop.html>

OCTOBER 21-24ISGNSS 2014/KGS 2014Jeju, KoreaThe International Symposium on Global Satellite Navigation Systems and Korea GNSS Society Conference will be held jointly. The theme is “Cloud PNT in IoT.” <http://isgnss2014.org/>

NovemberNOVEMBER 3-5TRIMBLE DIMENSIONS 2014Las Vegas, Nevada USAThe Trimble International User Conference covers products using GPS technology - among many others- developed and sold by its network of companies. <trimbledimensions.com/>

NOVEMBER 9-14ICG-9Prague, Czech RepublicNinth Meeting of the International Committee on GNSS, a UN-backed group of GNSS and augmentation provider countries. <oosa.unvienna.org/oosa/en/SAP/gnss/icg/meetings.html>

NOVEMBER 20–21UPINLBS 2014 Corpus Christi, Texas USA The third IEEE international conference on “Ubiquitous Positioning, Indoor Navigation and Location-Based Services” will concentrate on innovative, state-of-the-art solutions and techniques that provide PNT capability anywhere, anytime. <http://upinlbs.tamucc.edu/>

DecemberDECEMBER 1–4PTTI 2014 Boston, Massachusetts USAThe 46th systems and applications meeting for Precise Time and Time Interval managers, system engineers and program planners, will be held at the The Seaport Hotel in Boston. <http://www.ion.org/ptti/index.cfm>

DECEMBER 1–5UN/ ICG WORKSHOP: GNSS FOR SCIENTIFIC APPLICATIONSTrieste, ItalyA weeklong workshop on GNSS and its scientific applications in low-latitude regions of the world will be held in Trieste, Italy at the Abdus Salam International Centre for Theoretical Physics (ICTP). <http://www.oosa.unvienna.org/oosa/en/SAP/act2014/trieste-gnss/index.html>

DECEMBER 3–5NAVITEC 2014: ESA WORKSHOP ON SATELLITE NAVIGATION TECHNOLOGIES AND GNSS SIGNALS AND SIGNAL PROCESSING Noordwijk, The Netherlands“Era of Galileo IOV” is the theme of the 7th ESA Workshop on Satellite Navigation Technologies. < http://www.congrexprojects.com/2014-events/14c12/introduction>

DECEMBER 8–11NAVTECHGPS WINTER GNSS TRAININGSan Diego, California, USANavtechGPS Winter GNSS Training 2014 will take place at the Bay Club Hotel and Marina in San Diego, California. <http://www.navtechgps.com/events/location/>


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http://www.insidegnss.com/events

http://www.ainegypt.org/

http://www.gps.gov/cgsic/meetings/2014/

http://www.ion.org/gnss/

http://www.intergeo.de/intergeo-en/

http://isgnss2014.org/

http://www.trimbledimensions.com/

http://upinlbs.tamucc.edu/

http://www.ion.org/ptti/index.cfm

http://www.oosa.unvienna.org/oosa/en/SAP/act2014/trieste-gnss/index.html

http://www.congrexprojects.com/2014-events/14c12/introduction

http://www.navtechgps.com/events/location/

http://congress-era-glonass.com/

http://multignss.asia/workshop.html

http://www.oosa.unvienna.org/oosa/en/SAP/gnss/icg/meetings.html






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Inside GNSS Magazine : Off the shelf and into Space, Volume 9 Number 4 July/August 2014

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Transcript of Inside GNSS Magazine : Off the shelf and into Space, Volume 9 Number 4 July/August 2014