Predicting an upper bound on SAR ATR performance

IEEE Transactions on Aerospace and Electronic Systems

Volumn 37, Issue 3, 2001, Pages 876-888

  Boshra, Michael ^a,b   Bhanu, Bir ^a,b  

^a IEEE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MODEL TARGETS;

AUTOMATIC TARGET RECOGNITION; DATA REDUCTION; PROBABILITY; RADAR CLUTTER; RADAR IMAGING; SIGNAL DISTORTION; SYNTHETIC APERTURE RADAR;

RADAR TARGET RECOGNITION;

EID: 0035401533     PISSN: 00189251     EISSN: None     Source Type: Journal    
DOI: 10.1109/7.953243     Document Type: Article

References (20)

1. Automatic target recognition: State of the art survey
2. Introduction to the special issue on automatic target detection and recognition
3. Recognizing target variants and articulations in synthetic aperture radar images
4. Image understanding research for automatic target recognition
5. Predicting performance of object recognition
6. Validation of SAR ATR performance prediction using learned distortion models
7. Performance modeling for automatic target recognition systems
8. Classification performance prediction using parametric scattering feature models
9. Upper bound calculations of ATR performance for Ladar sensors
10. A probabilistic optimization approach to SAR feature matching
11. Hilbert-Schmidt lower bounds for estimators on matrix lie groups for ATR
12. Fundamental SAR ATR performance predictions for design trade, offs
13. Recognition of articulated and occluded objects
14. Bounding performance of peak-based target detectors
15. Performance of a high-resolution polarimetric SAR automatic target recognition system
16. Aided and automatic target recognition based upon sensory inputs from image forming systems
17. Standard SAR ATR evaluation experiments using the MSTAR public release data set
18. Search algorithms for model-based SAR ATR