Lori WesterkampSensor ATR Technology Division

Sensors DirectorateAir Force Research Laboratory

Sensors Directorate and ATR Overviewfor Integrated Fusion, Performance Prediction, and Sensor

Management for ATE MURI

21 July 2006

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AFRLMGen T. Bowlds

XPStaff

AirVehicles

SpaceVehicles Information Munitions Directed

Energy

AFOSRMaterials &

Manu-facturing

Sensors Propulsion HumanEffectiveness

AFRL Technology Directorates

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Sensors Directorate

Our Mission

Robust sensors and adaptive countermeasures that guarantee complete freedom of air and space

operations for our forces, and deny these capabilities to our adversaries at times and places

of our choosing.

To lead the discovery, development, and integration of affordable sensor and countermeasure

technologies for our warfighters.

Our Vision

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• Sense, identify, and track all air and surface targets and threats world wide and in all weather

• Counter “difficult” targets (WMD, hidden, LO)

• Protect air and space assets

• Control the battlespace electromagnetic spectrum

• Rapidly prosecute time-critical targets and threats

Priority Warfighter NeedsBeing Addressed by Sensors Directorate

Balance: Performance & Affordability

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Sensors Directorate Technology Thrusts

Radio Frequency Sensors &

Countermeasures

Electro-Optical Sensors &

Countermeasures

Automatic Target Recognition

& Sensor Fusion

• Signatures & Modeling• Assessment &

Foundation• Innovative Algorithms

Enabling Sub-thrusts

• Radio Frequency Apertures• Algorithms & Phenomenology• Digital Receivers & Exciters• Reference Systems• Components

• Transmitters & Receivers

• Phenomenology &Algorithms

• Optical Apertures

Application Sub-thrusts • Battlespace Access• Persistent ISR of the Battlespace• Prosecution of Time Sensitive Targets

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Potential Growth Areas

• Hidden Target Detection & Identification

• Automatic Target Recognition (ATR)& Sensor Fusion

• Electronic Warfare

• Time Critical Targeting (TCT)

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ATR Scope

GEOLOCATE

FIND

TRACK

ID M60

FUN

CTI

ON

S

SENSORS

MATURATION PROCESSAlgorithms

Signatures

Assessment0 100

1

PD

FAR

8

Challenge ProblemsStandard MetricsATR Theory

Characterized PerformanceHigh Performance ComputingOperational Databases

ASSESSMENT& FOUNDATION

Sensor Data Management System (SDMS)

ATR Approach

INNOVATIVE ALGORITHMS

FIND FIX TRACK & ID

Phenomenology ExplorationEM ModelingSynthetic Data

Operational Target Models/Databases

SIGNATURES & MODELING

SignatureCenter

5

10

15

20

10

20

30

40

02.5

5

7.5

10

5

10

15

20

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ATR Facilities

0 100

1

PD

FAR

PDPID

Sensor Resolution

Sensor Data Management System (SDMS)

Signature Center

Advanced Recognition Capability (ARC) Facility

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Operating Conditions (OCs)

TargetsTargetsSensorsSensors

InteractionsInteractions

EnvironmentEnvironment

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How can we make ATR easier?

• “Better” Features– Greater

discriminating power– Less variability

• “Better” Data– Resolution, dwell,

persistence, …– Multiple sources

(modes, looks, phenomenologies)

Often conflicting!

Fuel DrumsSide Fenders

Smoke Canisters

Length 6.9 mWidth 3.6 mHeight 2.2 m

Length 6.6 mWidth 3.5 mHeight 2.4 m

T-72 T-62

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ATR-Driven Sensing

Cueing, Prioritization for the Human

~1’10’ ~1’

Short Circuits

Resonant Cross Slots Cavity

Low Dielectric/Low Loss Face Sheet

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‘Clear Box’ View of ATR

0 100

1

PD

FAR

Environment

DetectDetectTrackTrack

GeolocateGeolocateIDID

SensorTargetATR

DecisionsHuman

Decisions

TrainedFeatures Templates Models

Target Knowledge

FeatureExtractor

Discriminator

DecisionRule

Target Knowledge

Target Models &Database

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Multiple Sensor ATR

Target Models &DatabaseSensor

Model

Environment

DetectDetectTrackTrack

GeolocateGeolocateIDID

Sensor(s)TargetATR

DecisionsHuman

Decisions

Sensor Management Registration

Environment Model

Performance Model

Adaptation

Behavior Models

Anticipate

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ATR Theory GoalsATR Theory Goals

• Performance Prediction– Algorithm (or feature) dependent – As a function of sensing, target, and environment conditions– Tool for developers (how will this new feature help?),

“maturers” (does this work well enough for my application?), and users (is this system going to work in my scenario today?)

• Performance Bounds– Algorithm independent performance limits– As a function of sensing, target, and environment conditions– Is the limit the algorithm (keep working) or the sensor

(motivate sensor improvements)?• Performance Models

– Packaged tools from Performance Prediction usable by sensor management and fusion algorithms to weigh sensor contribution

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Growing ATR Complexity

Environments

Targets

Military Vehicles

Aircraft

Civilian Vehicles

Dismounts

Open Forested Urban

Function

Tougher ClutterMore Obscuration

SmallerMore Variable

Detect

Track

ID

More S

pecif

ic