A NATO Arctic Research Profile - European Space Agency 12.03.12.pdf · Via Supply Chain for...

Ed Gough Chief Scientist

NATO Undersea Research Center La Spezia, Italy

A NATO Arctic Research Profile

NATO UNCLASSIFIED

Maritime Environment: North Atlantic

Downward Irradiance [W m-2 nm-1]: Top of Atmosphere to 100 meter depth

The Ocean is dark and cold

Arctic provides a Unique Acoustic Environment

• Sound speed minimum at surface

• Ice cover minimizes dynamics due to atmospheric forcing

• Quietest and loudest noise environment

Iconic Arctic Naval Asset

Medea: the Gore Box

Magnuson Park: Seattle

Future of Arctic Submarine Science

Characterized by Cooperation

• Pooling and Sharing – NATO’s center for research and

experimentation to support maritime operations & research for NATO and the Nations

• Prioritization – Conducts basic research (Science) and

apply that knowledge to emergent problems (Technology)

• Specialization – Conducts trials at sea to discover new

knowledge and to test hypotheses & technologies in difficult environments

NURC to become Centre for Maritime Research and Experimentation

MSD, 2011-06-16 NATO S&T Organization 10

Collaboration At Sea

11

NURC UxVs ASSETS

13

MUSCLE AUV: Demonstrator for Autonomous Mine Search

Tailcone

Drop-weight

USBL transponder

Battery Pack

SAS electronics housing

SAS transmit arraySAS receive arrays

WiLan antenna

GPS L1/L2 antenna

Main Electronics Housing (MEH)Doppler Velocity Log (DVL)

RF Modem, RDF Beacon, GPSAcoustic ModemAcoustic Abort System

LBL-beaconLift eye

Shore power access panel

Battery PackCTD sensor

• State-of-the-art synthetic aperture sonar (SAS) on autonomous vehicle – 300 kHz centre frequency, 60 kHz bandwidth – 2.5 cm resolution up to 200 m range

NETWORK-ENABLED FUTURES

PROCESS, ANALYZE, VALIDATE

Authoritative ProductsNATO STANDARDS (NNEC), OPEN STANDARDS

Analysis & Development

• Collaboration • Governance • Written documents

• Highly-skilled interdisciplinary professionals – with physics, engineering, mathematics backgrounds and – sea-going experience, – recruited from NATO nations.

• Over 50 years experience performing controlled measurements at sea

• Unique expertise in research, development, testing and evaluation of ocean and maritime concepts and equipment.

• Visiting Scientists and Joint Research Program

The NURC Team

MSD, 2011-06-16 NATO UNCLASSIFIED 15

NURC: A Program to Manage Risk

16

• Instrumentation • Platforms • Data • Visits

• Workshops

• Publication • Conferences • Exchanges

• Prototypes • Demonstrations • Experimentation

• Operational • Doctrinal or • Acquisition landing pad

Via Supply Chain for Innovation

Knowledge Sourcing Concept Generation

Capability Development

Experimentation & Demonstration Diffusion

Operational

Synthetic

Science Technology

Theory TRL 0 1 2 3 4 5 6 7 8 9 Observe Analyze Validate

Organize Synthesize Model Simulation Smart Games Forecasts

Maritime Science & Technology: A Framework

Operational

Synthetic

Science Technology



Maritime Science & Technology: A Framework

PROCESS, ANALYZE, VALIDATE

Authoritative Products

Operational

Synthetic

Science Technology



Maritime Science & Technology

• Observation-based • Hypothesis-based • Invention-driven • Validation & verification

• Data & Analysis • Models & Synthesis • Algorithms • Simulations • Theory • Prediction & Forecast

• Autonomous futures • Distributed/ netted futures • Operational experimentation &

demonstration

• Simulation • Smart games • Decision Aids • Battlespace environments (EKOE) • Validation & verification

Environmental Knowledge and Operational Effectiveness

Science Technology

Real

Virtual

Battlespace Characterization

Tactical Prediction

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2000

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Latitude [deg]Longitude [deg]

Dep

th [m

]

Bathymetry [m]Optimizedglider tracks sol# 1

10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'

10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'

Objectives: Pc=0.060025Duration [h]=47.9Track bathy score [Km2]=284.8026Optimized params: Climbing target depth [m] = 20Surfacing time [h] = 3.346

0

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1000m bathymetryReference missionPareto solution #1

Decision Support

Groom GLASS

OSS Sat. Prod.

SWA

Marine radar Tropical cyclones

Knowledge of Operational Environments U

ncer

tain

ties

prop

agat

ion

Feed

back

loop

and

miti

gatio

n Tier 3 – the Decision Layer • Options / Courses of Action • Quantify Risk

Tier 2 – the Performance Layer • Operational effectiv.

Tier 1 – the Environment Layer • Search patterns • Asset alloc. Ensemble Pred. Wave Pred. Meteo. Pred. Ocean. Pred.

Prob. of Detection

Adaptive Sampl. Glider Decis. Supp.

Ocean Ensemble Forecast TL

Glider L&R

C&C Centre Water-space Mgmt

Glider Data

SST L-band Rx

Satellites MSD, 2011-06-16 21 NATO UNCLASSIFIED

Autonomous Naval Mine Countermeasures

Science Technology

Virtual

Real

Autonomous HFSAS

High res. LF SAS

Swed MCM Ex. New concepts for mine neutralization

ICARUS NEST

Autonomous ISR

Science Technology

Real

Virtual

Communications/networks in the marine environment

Decision Support

Concepts for littoral undersea surveillance

Maritime Security

Science Technology

Real

Virtual

Maritime Situational Awareness

Port Protection

NEREIDS

Symposia

NAVTRONIC

ETD

Science Technology

Virtual

Real UW Modem

Passive acoustic

Passive acoustic

ARGOMARINE

CINNAMON

26

Discussion

Objectives

Technical Approach Products

• Develop techniques to operate gliders in an efficient and cost-effective manner • Investigate the implementation of gliders into the Global Ocean Observing System (GOOS) • Define the scientific, technological and organizational/legal levels for a European glider capacity for research

• Implementation of new sensor and data storage technologies into gliders • Assimilation of glider data into ocean prediction models • Development of innovative techniques for glider mission design • Planning, executing and reviewing missions involving a fleet of gliders

• Methodologies to design for optimal sampling strategies with gliders • Risk assessment tool for glider missions • System to design glider missions based on optimization criteria and constraints

Gliders for Research, Ocean Observation, and Management (GROOM)

Update RS_JO 15022012

Objectives


• Develop an integrated decision support framework to enable:

the automation and effectiveness of information fusion

the assessment of risk due to the environment (and its uncertainty)

optimal decision making

• Environmental risk assessment and decision making employing state-of-the-art machine intelligence algorithms • Asset deployment through the optimization of mission specific objectives by using multi-objective optimization algorithms

• Generic decision support framework for maritime operations to assess risk and its mitigation through asset placement • Prototype planning tool for glider operations, incorporating military objectives • Improved risk assessment algorithms for counter piracy operations and mission asset placement

Decisions in Uncertain Ocean Environments


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10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'

10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'


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Objectives


• Collect environmental data through networked sensors and remote sensing • Optimize the information content and cost of observational networks • Process environmental data to generate synoptic views of the battlespace in a timely manner

• Characterization of seabed properties with fixed and mobile sensors/platforms • Characterization of sea surface conditions with gliders, marine radar and synthetic aperture radar • Implementation of dynamically constrained data fusion methods • Develop autonomous reactive capabilities for gliders

• Validated procedures to infer sea surface wind conditions from marine radar • Technique to assess sea state conditions in denied areas using gliders • Implement algorithms for reactive sampling behavior of a glider • Collection of data sets of relative seabed acoustic reflectivity and interface roughness



Objectives


• Develop an algorithm to retrieve surface winds from marine radar data collected with fixed or moving vessels • Develop and test an algorithm to retrieve wind fields and wind gusts from sequences of marine radar images

• Infer wind speed and direction from marine radar intensity • Infer wind direction from wind streaks and movements of intensity patterns in marine radar image sequences • Test and validate the developed algorithms utilizing the data collected during the ONR HiRes experiment

• Tested and validated algorithm to retrieve surface wind vector from marine radar data • Tested and validated algorithm to infer wind fields and wind gusts from marine radar data

Marine Radar Winds


Estimating Satellite Uncertainty


Objectives


• Develop a software tool (with graphical user interface) for calibrating satellite imagery in comparison with ground truth and estimating uncertainty. • Apply the tool to maximize improvement (lower uncertainty) in radiometric retrievals from the Visible Infrared Imager Radiometer Suite (VIIRS)

• Evaluate covariance fields in SST and ocean color imagery to determine spatial uncertainty • Obtain time series of satellite imagery and in situ data, merging them with the software tool • Develop an “Uncertainty Index” and determine its seasonal value in selected areas

• MatLab GUI tool/software with user manual • Website delivery of near real-time uncertainty analyses for selected and qualified regions (‘Golden Regions’) • Inter-comparison of satellite systems (VIIRS, MODIS and MERIS) • Data base of in situ bio-optical data collected from NURC sponsored cruises in the Mediterranean Sea

Glider Acoustic Sensing of Sediments (GLASS)


Objectives


• Estimate the geo-acoustic properties of sediments using ambient noise and gliders

• Install tetrahedral hydrophone arrays on two types of gliders (Slocum and Folaga)

• Investigate the feasibility of estimating sediment properties using this array configuration in sea-trials

• Measure potential noise sources that would limit the effectiveness of the technique (e.g., flow noise and pump noise)

• A capability to discreetly survey seafloor properties in shallow water areas using long endurance autonomous vehicles

• Demonstrate the capability in a NATO exercise.

Objectives

• Develop predictive capabilities in ocean physics, ocean optics, and ocean acoustics • Improve coastal ocean predictions by assimilating in situ data • Understand the transfer of uncertainty, from environment to application

Technical Approach

• Implement the Regional Ocean Model System (ROMS), test and validate with data sets collected during NURC sea- trials • Implement an oceanographic data assimilation scheme based on the ensemble Kalman Filter • Evaluate the relative sensitivity of various physical properties on optical transmission and acoustic propagation

Products

• Tools to estimate environmental uncertainty, relative parameter sensitivity, and the transfer of uncertainty to application domains • A (pre)–operational coastal ocean prediction system for military applications • An ocean optical- physical (-ecosystem) model

Tactical Prediction


Ocean Strategic Services Beyond 2015 (OSS2015)

• Relate remotely sensed ocean color to in- water bio-optical properties and their vertical distribution, by combining state-of-the-art bio-optical profilers and earth observations (EO) • Develop assimilation schemes to ingest EO and in situ ocean color data into innovative numerical models (bio-optical and biogeochemical)

• Vertically extrapolate near-surface remotely sensed water-leaving radiances • Convert these spectral radiances into inherent optical properties (IOPs) using inverse/forward radiative transfer modeling • Deconvolute these spectral IOPs into optically active constituents (OACs) • Develop a fully coupled optical-ecosystem-physical model to estimate ocean color radiances using OACs

• Report describing the required in situ datasets for this modeling effort • Technical note describing the input data sets and proposed modeling approach • In situ Database Status Report • Testing the assimilation capability of the Harvard Ocean Prediction System (HOPS) model in the Ligurian Sea using remotely sensed chlorophyll concentrations


Objectives


NURC Program of Work

Maritime Situational Awareness

Port Protection

Autonomous HFSAS

New concepts for mine neutralization

High res. LF SAS

Communications/networks in the marine environment

Decision Support

Concepts for littoral undersea surveillance

MMRM


Tactical Prediction

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10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'

10' 20' 15oE 30.00'

40' 50' 24'

36'

48'

37oN

12'

24'


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Decision Support UW Modem

Passive acoustic

Passive acoustic

ARGOMARINE

• Thanks.

A NATO Arctic Research Profile - European Space Agency 12.03.12.pdf · Via Supply Chain for...

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