Vector quantization and learning vector quantization for radar target classification

Clayton V. Stewart; Yi-Chuan Lu; Victor J. Larson

doi:10.1117/12.160585

15 October 1993 Vector quantization and learning vector quantization for radar target classification

Clayton V. Stewart, Yi-Chuan Lu, Victor J. Larson

Proceedings Volume 1960, Automatic Object Recognition III; (1993) https://doi.org/10.1117/12.160585
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

Radar target classification performance is greatly dependent on how the classifier represents the strongly angle dependent radar target signatures. This paper compares the performance of classifiers that represent radar target signatures using vector quantization (VQ) and learning vector quantization (LVQ). The classifier performance is evaluated with a set of high resolution millimeter-wave radar data from four ground vehicles (Camaro, van, pickup, and bulldozer). LVQ explicitly includes classification performance in its data representation criterion, whereas VQ only makes use of a distortion measure such as mean square distance. The classifier that uses LVQ to represent the radar data has a much higher probability of correct classification than VQ.

Citation Download Citation

Clayton V. Stewart, Yi-Chuan Lu, and Victor J. Larson "Vector quantization and learning vector quantization for radar target classification", Proc. SPIE 1960, Automatic Object Recognition III, (15 October 1993); https://doi.org/10.1117/12.160585

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