1. Bala Lakshminarayanan AUTOMATIC TARGET RECOGNITION April 1, 2004

2. Bala Lakshminarayanan Introduction Automatic Target Recognition ATR process –Detection –Tracking –Feature extraction –Identification / Recognition

3. Bala Lakshminarayanan Motivation Why ATR –Reduce human workload –Repetitive tasks –Limited vision of humans vs multi feature Where ATR

4. Bala Lakshminarayanan 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

5. Bala Lakshminarayanan ATR…(1) Target Clutter Background variation, scene variation Target variations, new targets, Parameters  Brightness, Temperature, Range/Distance, Velocity….

6. Bala Lakshminarayanan ATR…(2) Techniques involved –Sensor development –Algorithm development –Statistical pattern recognition –Adaptive learning –Neural networks –Image processing

7. Bala Lakshminarayanan ATR…(3) ATR classification By human-machine task sharing –Aided –Autonomous By range of output values –Binary –Multi valued

8. Bala Lakshminarayanan ATR…(3) Requirements –High resolution sensors –High speed processors –Collateral information –Low false positives –Real time operation –Recognition of new targets –Clutter independence

9. Bala Lakshminarayanan 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

10. Bala Lakshminarayanan 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

11. Bala Lakshminarayanan Sensors for ATR…(3) SensorTimeWeatherResolutionRangeParameter VisibleDay timeConstrainedLow/mediumLimitedBrightness FLIRDay/nightConstrainedLow/mediumMedium (10- 15km) Temperature AcousticDay/nightMedium dependent LowLimited (in meters) Distance LASERDay/nightConstrainedHighMedium (5km) Range/veloci ty/3D shape RADARDay/nightAllHigh Distance/vel ocity ….disadvantages of different sensors

12. Bala Lakshminarayanan Sensors for ATR…(4) New sensors –LADAR –SAR –Multi sensor http://www.sandia.gov/RADAR/whatis.html

13. Bala Lakshminarayanan 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

14. Bala Lakshminarayanan 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

15. Bala Lakshminarayanan Performance measure…(2) SNR = (I t – I b )/I b –I t and I b are target and background intensities ROC –Plot of detection rate vs false alarm Confusion matrix Consistency

16. Bala Lakshminarayanan Performance measure…(3) Prob of detectionFalse alarm rate ATR SystemsMax0.86913.323 Min0.6043.532 Mean0.6888.195 Human SystemsMax0.8330.9 Min0.520.017 Mean0.6830.234

17. Bala Lakshminarayanan Performance measure…(4) Ground TruthSystemM60M113M35 M603/8 class0.67/0.170.21/0.120.12/0.0.5 Human0.7700 M1133/8 class0.08/0.020.66/0.250.27/0.12 Human0.020.650.03 M353/8 class0.18/0.120.21/0.070.61/0.36 Human0.010.060.85 Confusion matrix

18. Bala Lakshminarayanan Performance measure…(5) Improved measure Augustyn, “A new approach to Automatic Target Recognition” IEEE Trans on Aerospace and Electronic Systems

19. Bala Lakshminarayanan 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

20. Bala Lakshminarayanan Learning in ATR…(2) Objectives of learning –Identify data to a class –Accommodate new features –Accommodate new targets –Express inability to classify (new target)

21. Bala Lakshminarayanan 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

22. Bala Lakshminarayanan 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

23. Bala Lakshminarayanan Learning in ATR…(5) Theory Revision –Refines domain knowledge –Knowledge engineer can provide approximate theory –Addressed deficiency of EBL

24. Bala Lakshminarayanan 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

25. Bala Lakshminarayanan Questions & Comments