VRS Network The Magic Behind the Scene Xiaoming Chen Trimble Terrasat GmbH.

VRS Network The Magic Behind the Scene

Xiaoming Chen

Trimble Terrasat GmbH

Outline GNSS Positioning Error Sources General Introduction of Network RTK

– VRS– RTCM 3 Network Message

Use Network Correction Quality To Improve Rover Performance

Sparse Glonass Network Large Network Data Processing Summary

GNSS Positioning Error Sources

Orbir Error Satellite Clock Error

Îonosphere

Troposphere

Receiver Clock Error Multipath

GNSS Positioning Error Sources

Reference stations

Ionosphere

GNSS Network Models

Network RTK Utilize a reference station network to model

distance dependent errors in real-time Generate network corrected reference station

data/corrections and transmit to rover in real-time – VRS– FKP– RTCM Network Message

Rover use the network corrected data to achieve better performance over longer distance

Geometric Filter

Geometric Filter

Geometric Filter

Raw DataAnalysis

Synchronizer

Geometric Filter

Ionospheric Filters

Code-Carrier Filters

Ambiguity Search & Fix

Residual ManagementNetwork Model

IntegrityVRS/Net RTCM/FKP

Generation

Raw DataAnalysis

Raw DataAnalysis

Raw DataAnalysis

Geometric Filter

Network Processing Diagram

Virtual Reference Station (VRS)

Computes tropospheric, orbit and ionospheric models in real time.

Derives an optimized VRS correction stream derived from these models for each rover

Requires bi-directional communication, also works with rebroadcast/RTCM VRS module

Based on RTCM, CMR, CMRx. Low bandwidth required

Support GLONASS

RTCM Network Message RTCM 3.1 standard Broadcast solution Derive carrier ambiguities in network and generate

observations on one ambiguity level (no ambiguities in the Double Difference sense)

Master & Auxiliary station One master station Up to 31 auxiliary stations (ambiguity “free” observations) High bandwidth or lower rate for corrections

Network corrections are computed on the rover from a subset of the network

GPS only

Modeling Error SourcesServer Centric vs. Rover Centric

VRS = Server Centric Approach: Complex error models are used:– Ionospheric model– Tropospheric model

RTCM Network Message = Rover Centric Approach: – Interpolation in the rover

Modeling Error Sources: An Example for tropospheric modeling

AGNES Network, Switzerland on July 7, 2003, operated by Swisstopo with Trimble VRS Jungfraujoch as rover Nearest ref. station: Hohtenn

Station Height [m]

Jungfraujoch

3634

Hohtenn 985Sannen 1419Zimmerwald

956

Huttil 779Luzern 542Andermatt 2367Jungfraujoch

Modeling errors: VRS vs. RTCM Network Message

-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.3 0.5 0.7 0.9 1.1 1.3

GPS Hour

Ion

o. F

ree

re

sid

ua

ls [m

]

10

15

20

25

30

35

Ele

vatio

n [d

eg

ree

]

VRS

NetRTCM

Elevation

Iono-free Residuals for SV 05

Benefits with VRS

NetRTCM [mm]

VRS [mm]

Improv[%]

Mean[mm]

North -4.66 -3.30

East -4.29 -5.11

Height

-117.00 -41.34

Standard

Deviation

[mm]

Height

46.12 39.19 15.1

2D 30.83 26.67 13.9

3D 55.47 47.41 14.5

RMS[mm]

Height

125.76 56.96 54.7

2D 31.47 27.36 13.1

3D 129.64 63.19 51.3

Use Correction Quality to Improve Rover Performance

Network RTK correction considered as interpolated corrections between reference stations

Interpolation is not perfect depending on actual atmosphere conditions

RTK Network server process provides quality estimates for residual interpolation

Can be used by the RTK rover to optimize RTK performance

Sparse GLONASS networks with reduced GLONASS correction quality

Residual Error Description

RTK Network generates a description of the dispersive and non-dispersive error for each satellite

Consists of constant, distance and height dependent terms

2222 didici

220

220

20

20 hd hdc

Predicted Network Correction Quality (strong ionosphere)

Predicted Network Correction Quality (calm ionosphere)

Network used for Evaluation of Quality Information

24 h data (1Hz) 5 Stations 1 Rover (33 km

from 0272)

Ionospheric Residuals PRN 22

0

20

40

60

80

100

120

0 30 60 90 120 150 180 210 240 270 300

Time in Minutes

Ion

osp

her

ic e

ffec

t [m

m]

0

10

20

30

40

50

60

70

Ele

vati

on

[°]

Absolute residuals

Predicted sigmas

Elevation

55% of the DD residuals < predicted sigmas

Geometric Residuals PRN 22


0

5

10

15

20

25

30

35

40

45

50

0 30 60 90 120 150 180 210 240 270 300

Time in Minutes

Geo

met

ric

effe

ct [

mm

]

0

10

20

30

40

50

60

70

Ele

vati

on

[°]

Absolute residuals

Predicted sigmas

Elevation

0

20

40

60

80

100

120

140

0 30 60 90 120 150 180 210 240 270

Time in Minutes

Ion

osp

her

ic e

ffec

t [m

m]

0

10

20

30

40

50

60

70

Ele

vati

on

[°]

Absolute residuals

Predicted sigmas

Elevation





0

10

20

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60

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0 30 60 90 120 150 180 210 240 270

Time in Minutes

Ion

osp

her

ic e

ffec

t [m

m]

0

10

20

30

40

50

60

Ele

vati

on

[°]

Absolute residuals

Predicted sigmas

Elevation

Improving Rover Performance With Network Correction Quality

Predicted error statistics can help to improve positioning by– Better measurement weighting– Optimum combination of L1/L2 measurements

Helps to improve – Positioning accuracy – Ambiguity fixing

Positioning Error Comparison - East Error

Positioning Error Comparison – Height Error

Positioning Performance

average 3D-RMS (½ hour slots)

Sparse GLONASS Network

Increasing number of RTK network service providers introduce GLONASS only on selected stations

RTK Servers have to handle sparse GLONASS coverage in dense GPS networks

Provide high quality GPS correction and acceptable GLONASS correction

RTK Rover performance is better or equal to GPS only solution

GPS Network

GPS Only

GPS/GLN

GPS/Glonass Network

GPS Only

GPS/GLN

Partial GPS/GLONASS Network

GPS Only

GPS/GLN

Sparse Glonass Network

GPS Only

GPS/GLN

A Dense GPS/GLONASS Test Network

GPS&GLONASS

GPS only

A Sparse GPS/GLONASS Test Network

GPS&GLONASS

GPS only

Sparse GLONASS Test Results

Rover Initialization

Rover Positioning

Network Type 68%[sec] 95%[sec] No. Init.

GPS Only 14 18 2643

Dense GPS/GLN 12 15 2645

Sparse GPS/GLN 13 16 2644

Network Type RMS North

[mm]

RMS East

[mm]

RMS Height

[mm]

GPS Only 12 7 25

Dense GPS/GLN 12 6 23

Sparse GPS.GLN 12 6 23

Large GNSS Network Data Processing

Increasing Complexity and Demand… More Stations

– Tendency to increase networks to more than 100 stations – Challenge to process all data on one server in real-time (1Hz)

More Satellites– GPS– GLONASS– GALILEO

More Signals– L5– E5A, E5B

VRSNow Germany (145)

VRSNow Germany (Subnetwork)

VRSNow Germany (145)

Raw DataAnalysis

Synchronizer

Ionospheric Filters

Code-Carrier Filters

Ambiguity Search & Fix

Residual ManagementNetwork Model

IntegrityVRS/Net RTCM/FKP

Generation

Raw DataAnalysis

Raw DataAnalysis

Raw DataAnalysis

Geometric Filter

Geometric Filter

Geometric Filter

Geometric Filter

Geometric Filter

Network Processing Diagram

Centralized Geometry FilterProvide iono.-free ambiguity for network

ambiguity fixingProvide ZTD estimationAll states estimated in a big (centralized) filterTypical setup

ZTD per stationReceiver clock error per stationSatellite clock error per satelliteAmbiguity per station per satelliteOrbit error

Centralized Geometry filterNumber of States

1215

18

20

40

80

120

0

500

1000

1500

2000

2500

Centralized Geometry FilterNumber of multiplications

0

10

20

30

40

50

60

70

80

90

100

10 20 30 40 50 60 70 80 90 100 110 120

Number of stations

Num

of M

ultip

licat

ion

in B

illion

No. Multiplications

Cubic function

Principle of Federated Filter

A bank of local Kalman filters runs parallel. A central fusion processor computes an

optimal weighted least-square estimate of the common system states and their covariance

Then the result of the central fusion processor is fed back to each local filter

Parallel Computing

Simultaneuous use of multiple compute resources to solve a computational problem

Computation Time Comparison(4 Core Dell Precision 490)

0

10000

20000

30000

40000

50000

60000

0 1 2 3 4 5 6

Hour

CPU

Tim

e (m

sec)

Opt, No OMP

Fed, No OMP

Fed, OMP

Computation Time Comparison(4 Core Dell Precision 490)

CPU Time OMP vs No OMP

0

100

200

300

400

500

600

700

800

900

1000

0 50 100 150 200 250 300 350 400

CPU Time OMP (msec)

CPU

Tim

e No

OM

P (m

sec)

CPU Load (VRSNow Germany)

Summary

Quality measures for RTK network corrections significantly improve the rover performance Positioning improved by up to a factor of 2 Initialization time reduced by 30%

Sparse GLONASS network provides decent rover performance during low to medium iono activity

Large network processing provide seamless and homogeneous solution cross the whole network with balanced CPU loadReduce the complexity of network administration

VRS Network The Magic Behind the Scene Xiaoming Chen Trimble Terrasat GmbH.

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Transcript of VRS Network The Magic Behind the Scene Xiaoming Chen Trimble Terrasat GmbH.