A Stochastic Model-Based Approach to SAR ATR

1. A Stochastic Model-Based Approach to SAR ATR Lee Montagnino Electronic Systems and Signals Research Laboratory Department of Electrical and Systems Engineering Washington University St. Louis, Missouri Supported in part by ONR grant N00014-98-1-06-06

2. Presentation Overview • Problem Definition • Likelihood Approach to ATR • Conditionally Gamma Model • Conditionally K distribution • Azimuth Correlation Model • Conclusions Montagnino: SAR ATR

3. Problem Definition • Typical Recognition Scenario Imaging Platform Target Classifier Orientation Estimator Montagnino: SAR ATR

4. Problem Definition • Model-Based Recognition Target Classifier Orientation Estimator Montagnino: SAR ATR

5. Problem Definition • Model-Based Recognition Training Data Functional Estimation Scene and Sensor Physics Image Processing Inference Montagnino: SAR ATR

6. Problem Definition • Use Modular Software Test Bed to Perform: • Direct comparisons of different stochastic models • Performance analysis under a wide range of testing and training scenarios • Detailed study of performance vs. models and model parameters Montagnino: SAR ATR

7. Likelihood Approach to ATR • Target Class and Pose Estimates Montagnino: SAR ATR

8. Likelihood Approach to ATR • Generalized Likelihood Ratio Test and Maximum-A-Posteriori Estimation Montagnino: SAR ATR

9. MSTAR DATA SET • A collection of spotlight mode SAR images from a number of target classes • Using 4 target classes from the public release set • Using 10 target classes from the public release set • MSTAR Program sponsored by DARPA and Wright Laboratory Montagnino: SAR ATR

10. MSTAR Data Set • Partitioned into two subsets: • 17 ° depression images used for estimating likelihood functions • 15 ° depression images used for experimentally assessing performance • For testing, we assume a uniform prior on orientation and target class Montagnino: SAR ATR

11. Gamma Distribution • Multi-parameter distribution • Relaxation of the quarter-power normal model • Relates to Work in MSTAR Program at WPAFB Montagnino: SAR ATR

12. Gamma Estimates • Maximum Likelihood Estimates where Montagnino: SAR ATR

13. Gamma Results • Percentage of Correct Classification and Orientation Estimation Error Montagnino: SAR ATR

14. K Distribution • Multi-Parameter • Mixture model • Models Specular and Diffuse Reflectivity Montagnino: SAR ATR

15. K Estimates • Expectation-Maximization Montagnino: SAR ATR

16. K Results • Percentage of Correct Classification and Orientation Estimation Error Montagnino: SAR ATR

17. Azimuth Correlation • Radar data correlated in azimuth Montagnino: SAR ATR

18. Azimuth Correlation Functions • EM Algorithm to Find Estimates of Montagnino: SAR ATR

19. Azimuth Correlation Covariance Images Montagnino: SAR ATR

20. Azimuth Correlation Results • Percentage of Correct Classification and Orientation Estimation Error Montagnino: SAR ATR

21. Conclusions • Gamma Distribution Model • low recognition rates • poor orientation estimation • K distribution model • comparable recognition rates to the zero-mean conditionally Gaussian presented by DeVore Montagnino: SAR ATR

22. Conclusion • Azimuth Correlation • comparable recognition rates to the zero-mean conditionally Gaussian model presented by DeVore • best orientation estimation error rates of any distribution • correlation models don’t match actual data Montagnino: SAR ATR

23. Questions Montagnino: SAR ATR

24. References Montagnino: SAR ATR

25. References Montagnino: SAR ATR