New wideband radar target classification method based on neural learning and modified Euclidean metric

Yicheng Jiang; Ping Cheng; Yangkui Ou

doi:10.1117/12.441689

20 September 2001 New wideband radar target classification method based on neural learning and modified Euclidean metric

Yicheng Jiang, Ping Cheng, Yangkui Ou

Author Affiliations +

Proceedings Volume 4555, Neural Network and Distributed Processing; (2001) https://doi.org/10.1117/12.441689
Event: Multispectral Image Processing and Pattern Recognition, 2001, Wuhan, China

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.

Citation Download Citation

Yicheng Jiang, Ping Cheng, and Yangkui Ou "New wideband radar target classification method based on neural learning and modified Euclidean metric", Proc. SPIE 4555, Neural Network and Distributed Processing, (20 September 2001); https://doi.org/10.1117/12.441689

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