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Proceedings Paper

New wideband radar target classification method based on neural learning and modified Euclidean metric
Author(s): Yicheng Jiang; Ping Cheng; Yangkui Ou
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Paper Abstract

A new method for target classification of high-range resolution radar is proposed. It tries to use neural learning to obtain invariant subclass features of training range profiles. A modified Euclidean metric based on the Box-Cox transformation technique is investigated for Nearest Neighbor target classification improvement. The classification experiments using real radar data of three different aircraft have demonstrated that classification error can reduce 8% if this method proposed in this paper is chosen instead of the conventional method. The results of this paper have shown that by choosing an optimized metric, it is indeed possible to reduce the classification error without increasing the number of samples.

Paper Details

Date Published: 20 September 2001
PDF: 4 pages
Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); doi: 10.1117/12.441689
Show Author Affiliations
Yicheng Jiang, Harbin Institute of Technology (China)
Ping Cheng, Harbin Institute of Technology (China)
Yangkui Ou, Harbin Institute of Technology (China)

Published in SPIE Proceedings Vol. 4555:
Neural Network and Distributed Processing
Xubang Shen; Jianguo Liu, Editor(s)

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