information fusion in undersea warfare -...

M. J. Larkin FUSION99 7/6/99 [email protected] 1

INFORMATION FUSION IN UNDERSEA WARFARE

Presented at:The International Conference on Information Fusion

Sunnyvale, CA

Michael J. LarkinNaval Undersea Warfare Center

Newport, RI [email protected]

6 July 1999

INFORMATION FUSION IN UNDERSEA WARFARE

Presented at:The International Conference on Information Fusion

Sunnyvale, CA

Michael J. LarkinNaval Undersea Warfare Center

Newport, RI [email protected]

6 July 1999


COLD WAR• Enemy: USSR

• Deep Water ASW• Main threat: Nuclear

Submarines• Passive ASW Dominant• Relatively Constant

Watermass Properties

UNDERSEA WARFARE-THEN AND NOW

UNDERSEA WARFARE-THEN AND NOW

TODAY• Iran, Iraq, Libya, Serbia, N. Korea,

etc., etc.

• Shallow Water USW• Diesel-electric submarines; mines

• Renaissance of Active USW• Highly Variable Watermass

Properties


DD-21 Operational RequirementsDD-21 Operational Requirements

• Submarine detection/engagement;• Torpedo defense;• On-board (in-stride) mine detection/avoidance;• Off-board mine reconnaissance;• Survivability;• Signature reduction;• Aviation mission support;• Interoperability with off-board USW sensors, models, and

databases.


The Littoral EnvironmentThe Littoral Environment

Humidity-Refractive EffectsHumidity-Refractive Effects

Wind Speed/DirectionWind Speed/Direction

Sensible and Latent HeatSensible and Latent Heat

Land CoverLand CoverWind-DrivenWind-Driven Circulation Circulation

Tidal PulseTidal Pulse

Coastal ConfigurationCoastal Configuration

Slope (Sea Floor)Slope (Sea Floor) BiologicsBiologics

Air TemperatureAir Temperature

TrafficabilityTrafficability

Sediment TransportSediment Transport

TerrainTerrain

AerosolsAerosols

Swell/Wave RefractionSwell/Wave RefractionIsland FlowIsland Flow

StraitsStraits IceIce

WavesWaves SurfSurfRain RateRain Rate

FogFog

Reefs, Bars, ChannelsReefs, Bars, Channels

Beach ProfileBeach Profile

DustDust

Soil MoistureSoil Moisture

Shelf/Internal WavesShelf/Internal Waves

TurbidityTurbidity

Hydrography - Fine ScalesHydrography - Fine Scales

Watermass Thermal - Watermass Thermal - Acoustic StructureAcoustic Structure

Sub-Bottom Structure Sub-Bottom Structure Real-time data,on-scene processing

required”NETWORK CENTRIC OCEANOGRAPHY IN USW” RADM W.G. Ellis 2 MAR 99


IUSW FocusIUSW Focus

• Improve USW performance in shallow water bydemonstrating a lightweight variable-depth transmitter andbroadband signal processing.

• Reduce USW manning by developing technology forbroadband signal processing, automated detection-classification-localization, data fusion, and environmentaladaptation.

• Provide in-stride mine detection and avoidance capabilitiesthrough the use of organic (on-board) sensors.

• Develop concepts to minimize impact on the DD-21preliminary hull design by studies focused on, for example,variable-depth sonar, multiple towed bodies, and conformal-array technology.


Platform Acoustic Warfare DataFusion (PAWDF)

Platform Acoustic Warfare DataFusion (PAWDF)

Program goals:

• Improve operator ability to detect, track and classifytargets in a highly cluttered environment

• Reduce quantity of data that must be processed by thesonar operator

• Reduce false alarm rate

• Improve quality of data

• Reduce number of sonar operators required

These goals are to be accomplished through development ofautomated, real-time data fusion algorithms and techniquesemploying Bayesian inference networks (BINs) and probabilisticreasoning to automate the fusion of all acoustic and non-acousticdata on a surface ship or a submarine at the sonar operator level.


Overview of Data Fusion AlgorithmOverview of Data Fusion Algorithm

• Historically, automatic acoustic contact fusion problem solvedusing strictly geometric measures (e.g., Mahalanobis distance)-these measures tend to be bias-limited and underutilize the data.

• This algorithm mitigates problem by including sonar operatorclues in similarity assessment process

• Three steps in process• Evidence extraction- geometric and non-geometric clues used by

operators are extracted from contact pair data and converted toevidence.

• Belief generation- for each contact pair, extracted evidenceprocessed through a Bayesian influence network to produce anestimate (belief) of the probability of association.

• Cluster formation and management- uses positive and negativeinformation to produce a clustering of contacts based on beliefsobtained in the previous step.


Information Exploited by Human OperatorsInformation Exploited by Human Operators

• Common frequency dynamics• common SNR dynamics• common detection times• common classification clues• a priori knowledge


Data AssociationData Association

1. Form “current clusters” based on the mostrecent belief estimates

2. Calculate fuzzy comparison values betweencurrent and reference clusters

3. Calculate optimal current cluster- referencecluster matching

4. Determine contact-to-reference clusterassignments


Multi-Sensor InputsMulti-Sensor Inputs

ACTIVE - CW

ACTIVE - FM

PASSIVE

RADAR

ENVIRONMENTAL,BATHYMETRY,WRECKS, ETC.

CLASSIFY:SUB/NON-SUB

MULTI-SENSOR

CLASSIFIER

ASSOCIATION IS DONE BY MANDF

Initial emphasis here


How to Combine These SensorsHow to Combine These Sensors

• Use Active CW and FM separately, or fused CW-FM?• Multiple active waveforms• Utilization of passive sonar (initially, may just consider as a

binary input)• Incorporating radar

– Also a binary input– Radar classifier may provide more information– Although radar primarily thought of in this context as verifying

a surface contact, might also be used to identify submarinenear surface

• Information from other sources (e.g., correlation of an activereturn with the position of a known wreck)


Architecture OptionsArchitecture Options

αα FEATURES

ββ FEATURES

αα CLASSIFIER

ββ CLASSIFIER

αα - ββCLASSIFIER

C

B

A = DATA LEVEL B = FEATURE LEVEL C = DECISION LEVEL

αα - ββCLASSIFIER A

SENSOR αα

SENSOR ββ


Levels of FusionLevels of Fusion• Data level fusion (A)

– Raw data combined and a single feature vector is extracted– Classification performed on feature vector using clustering, neural

network, etc.– Theoretically, produces most accurate results– Requires that data be commensurate and properly associated

• Feature level fusion (B)– Feature vector extracted at each sensor, concatenated into a single

feature vector– Still requires data alignment and association/correlation

• Decision level fusion (C)– Feature extraction and classification done at sensor level– Classification decisions combined using techniques such as classical

or Bayesian inference, weighted decisions, Dempster-Shafer, etc.

• Hybrid Approach– Fusion done either at more than one level simultaneously, or at varying

levels as determined by environment, available sensors, etc.– Most adaptable, but overhead required to monitor fusion process


Active Waveform ConfigurationActive Waveform Configuration

53CWDT, NORM

FBE , CFEADT

WDT, NORMFBE , CFE

ADT

CW

FMMANDF Level 1

CW-FM MPC


ETC Features Used for ClassificationETC Features Used for Classification

CW Features• Mean SNR• Std. Dev. Amplitude• Amplitude Coeff. of Variation• Mean Weighted Cross Range

Extent• Variance of Weighted Cross

Range Extent• Weighted Mean Doppler

FM Features• Amplitude Features (23)

– Number of peaks– Amplitude kurtosis– Total energy above threshold

• Frequency Features (24)– Mean frequency within band– Frequency difference n = 1,…,5

– Average bandwidth crossing

• Beamformer (Time-Frequency)Features (17)– Difference frequency mean n– Difference frequency STD n– Histogram BF freq. data [a,b]


Feature-Based Sequential ClassifierFeature-Based Sequential Classifier

Assumptions:• PDFs for target and non-target classes are Gaussian.• Each vector of feature values computed from a data cluster

is independent of all other feature vectors (past, present andfuture scans).

• Each vector of feature values computed from a “snippet” ofdata is computed from either the target or the non-targetdistribution.

• It is possible that within a particular scan, several dataclusters (hence, several feature vectors) will be associatedto an established track.


Feature-Based Sequential ClassifierFeature-Based Sequential Classifier

Algorithm:• Cumulative test statistic- standard log-likelihood ratio.• Ratio of joint probability densities of feature vectors,

conditioned on alternative hypotheses (target present ornot) and number of feature vectors.

• Compute probability densities of feature vectorsconditioned on hypothesis that the ith feature vector camefrom the target echo (assuming independence).

• Derive the probability that the ith feature vector came fromthe target echo from known system parameters (Bar-Shalom) assuming:– Gaussian density functions– Uniform priors for number of non-target data clusters


Feature-Based SequentialClassifier - Algorithm (cont.)Feature-Based Sequential

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Extension of Sequential Classifier toMultiple Waveform Classification

Extension of Sequential Classifier toMultiple Waveform Classification

• Select feature set to be used with each waveform.• Extract features from multiple sensor observations.• Construct a single combined feature vector:

where α,β α,β refer to the two waveforms, and m(i), i = α,β α,β is thenumber of features for each of the waveforms.

• Features must be obtained from associated data.• Classification is now performed on the combined feature

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Multi-Sensor Classifier - Current ResultsMulti-Sensor Classifier - Current Results

• Implemented with simulated data to demonstrate efficacy ofapproach.

• Joint probability density functions of feature vectorsestimated from sample means/covariances of SWADC data.

• Testing on real data will be initiated this FY.• Multiple waveform data is presently available, and data will

be associated by MANDF.


Multi-Sensor Classifier - Future DirectionsMulti-Sensor Classifier - Future Directions

• Assess sensitivity of classification probability and false alarmrate to threshold setting.

• Finalize feature selection.

• Evaluate classifier with real data.

• Measure performance.

• Determine the value added by implementation of the multiplewaveform classifier.

information fusion in undersea warfare -...

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