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Proceedings Paper

Vector quantization and learning vector quantization for radar target classification
Author(s): Clayton V. Stewart; Yi-Chuan Lu; Victor J. Larson
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Paper 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.

Paper Details

Date Published: 15 October 1993
PDF: 10 pages
Proc. SPIE 1960, Automatic Object Recognition III, (15 October 1993); doi: 10.1117/12.160585
Show Author Affiliations
Clayton V. Stewart, George Mason Univ. (United States)
Yi-Chuan Lu, George Mason Univ. (United States)
Victor J. Larson, George Mason Univ. (United States)

Published in SPIE Proceedings Vol. 1960:
Automatic Object Recognition III
Firooz A. Sadjadi, Editor(s)

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