Bala Lakshminarayanan

AUTOMATIC TARGET RECOGNITION

April 1, 2004

Bala Lakshminarayanan

Introduction

• Automatic Target Recognition• ATR process

– Detection– Tracking– Feature extraction– Identification / Recognition

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Motivation

• Why ATR– Reduce human workload– Repetitive tasks– Limited vision of humans vs multi feature

• Where ATR

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Objectives

• Aided ATR– Detect targets in high clutter environment– Low false alarm rate– High detection rate

• Autonomous ATR– High true positives– Ability to recognize target accurately– Consistency– LOAL, FAF

Bala Lakshminarayanan

ATR…(1)

Target

Clutter

Background variation, scene variation

Target variations, new targets,

Parameters Brightness, Temperature, Range/Distance, Velocity….

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ATR…(2)

• Techniques involved– Sensor development– Algorithm development– Statistical pattern recognition– Adaptive learning– Neural networks– Image processing

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ATR…(3)

• ATR classification

• By human-machine task sharing– Aided– Autonomous

• By range of output values– Binary– Multi valued

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ATR…(3)

• Requirements– High resolution sensors– High speed processors– Collateral information– Low false positives– Real time operation– Recognition of new targets– Clutter independence

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Sensors for ATR…(1)

• Visible camera – Brightness

• Infra red camera – Surface Temperature

• Acoustic – Distance

• RADAR – Range, velocity

• LASER – Range, 3D shape

• Microwave / Millimeter Wave – Range

• Multispectral

• Multi-sensor ATR

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Sensors for ATR…(2)

• Active or passive sensors

• Criteria for sensors– All time operation– All weather operation– Range of sensor– Resolution– Parameter and ease of recognition

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Sensors for ATR…(3)

Sensor Time Weather Resolution Range Parameter

Visible Day time Constrained Low/medium Limited Brightness

FLIR Day/night Constrained Low/medium Medium (10-15km)

Temperature

Acoustic Day/night Medium dependent

Low Limited (in meters)

Distance

LASER Day/night Constrained High Medium (5km)

Range/velocity/3D

shape

RADAR Day/night All High High Distance/velocity

….disadvantages of different sensors

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Sensors for ATR…(4)

• New sensors– LADAR– SAR– Multi sensor

http://www.sandia.gov/RADAR/whatis.html

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Problems in ATR

• Feature selection

• Algorithms for good recognition

• Measurement units for performance

• Computational power

• Representative databases– Orientation, time of day, weather, new targets, clutter,

how much data, location, camouflage…

• Handling new targets (minimum distance classifiers)

• Overfitting

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Performance measure…(1)

• Probability of detection

• Probability of classification (tracked/wheeled)

• Probability of recognition (tank/armored carrier)

• Probability of identification (brand name)

• False alarm rate

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Performance measure…(2)

• SNR = (It – Ib)/Ib

– It and Ib are target and background intensities

• ROC– Plot of detection rate vs false alarm

• Confusion matrix

• Consistency

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Performance measure…(3)

Prob of detection False alarm rate

ATR Systems Max 0.869 13.323

Min 0.604 3.532

Mean 0.688 8.195

Human Systems Max 0.833 0.9

Min 0.52 0.017

Mean 0.683 0.234

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Performance measure…(4)

Ground Truth System M60 M113 M35

M60 3/8 class 0.67/0.17 0.21/0.12 0.12/0.0.5

Human 0.77 0 0

M113 3/8 class 0.08/0.02 0.66/0.25 0.27/0.12

Human 0.02 0.65 0.03

M35 3/8 class 0.18/0.12 0.21/0.07 0.61/0.36

Human 0.01 0.06 0.85

Confusion matrix

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Performance measure…(5)

Improved measure

Augustyn, “A new approach to Automatic Target Recognition” IEEE Trans on Aerospace and Electronic Systems

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Learning in ATR…(1)

• ATR learning areas– Initial acquisition of domain theory– Adapt domain theory to new situations -

“transfer”– Adapt new features

• Usually, supervised training occurs

• Need to use context based data

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Learning in ATR…(2)

• Objectives of learning – Identify data to a class– Accommodate new features– Accommodate new targets– Express inability to classify (new target)

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Learning in ATR…(3)

• ANNs – They model human brain– Feedforward or backpropagation networks– Backpropagation network is preferred since it

is robust– Adaptive learning

• Limitations– Adapting to new situations is cumbersome– Highly sensitive to noise, occlusion– Nearest neighbor technique

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Learning in ATR…(4)

• Explanation Based Learning– Machine derives explanation– 4 inputs example, goal, operationality

criterion (features), domain theory (relation)

• Examples can be generated using EBG– Irrelevant details removed from example– Explanation is generalized– Cannot learn new features– Difficult to implement

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Learning in ATR…(5)

• Theory Revision– Refines domain knowledge– Knowledge engineer can provide approximate

theory– Addressed deficiency of EBL

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New developments…(1)

• Multi sensor ATR– Optical limits have been reached– Collateral information implies better results

• Data fusion– Information fusion– Pixel fusion– Decision fusion

• Model based systems

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Questions

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