Evaluation of MACH and DCCF correlation filters for SAR ATR using the MSTAR public database

Abhijit Mahalanobis; Daniel W. Carlson; Bhagavatula Vijaya Kumar

doi:10.1117/12.321849

15 September 1998 Evaluation of MACH and DCCF correlation filters for SAR ATR using the MSTAR public database

Abhijit Mahalanobis, Daniel W. Carlson, Bhagavatula Vijaya Kumar

Proceedings Volume 3370, Algorithms for Synthetic Aperture Radar Imagery V; (1998) https://doi.org/10.1117/12.321849
Event: Aerospace/Defense Sensing and Controls, 1998, Orlando, FL, United States

Abstract

The MACH and DCCF correlation filter algorithms are evaluated using the publicly released MSTAR data base. These algorithms can be used as a matching engine for automatic target recognition in SAR imagery. In practice, the required filters can be synthesized using model based signature predictions. In addition, the MACH and DCCF algorithms are optimized to be robust to variations (distortions) in the target's signature. Unlike Matched Filtering or other exhaustive template based methods, the proposed approach requires very few filters. The paper describes the theory of the algorithm, key practical advantages and details of test results on the public MSTAR data base.

Citation Download Citation

Abhijit Mahalanobis, Daniel W. Carlson, and Bhagavatula Vijaya Kumar "Evaluation of MACH and DCCF correlation filters for SAR ATR using the MSTAR public database", Proc. SPIE 3370, Algorithms for Synthetic Aperture Radar Imagery V, (15 September 1998); https://doi.org/10.1117/12.321849

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