Improved template-based SAR ATR performance using learning vector quantization

Lance M. Kaplan; Romain Murenzi; Kameswara Rao Namuduri; Marvin N. Cohen

doi:10.1117/12.326763

14 October 1998 Improved template-based SAR ATR performance using learning vector quantization

Lance M. Kaplan, Romain Murenzi, Kameswara Rao Namuduri, Marvin N. Cohen

Proceedings Volume 3462, Radar Processing, Technology, and Applications III; (1998) https://doi.org/10.1117/12.326763
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

Abstract

This paper investigates methods to improve template-based synthetic aperture radar (SAR) automatic target recognition (ATR). The approach utilizes clustering methods motivated from the vector quantization (VQ) literature to search for templates that best represent the signature variability of target chips. The ATR performance using these new templates are compared to the performance using standard templates. For baseline SAR ATR, the templates are generated over uniform angular bins in the pose space. A merge method is able to generate templates that provide a nonuniform sampling of the pose space, and the templates produce modest gains in ATR performance over standard templates.

Citation Download Citation

Lance M. Kaplan, Romain Murenzi, Kameswara Rao Namuduri, and Marvin N. Cohen "Improved template-based SAR ATR performance using learning vector quantization", Proc. SPIE 3462, Radar Processing, Technology, and Applications III, (14 October 1998); https://doi.org/10.1117/12.326763

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