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Proceedings Paper

Combining wavelet transform and the evolutionary neural network for radar target recognition
Author(s): Ying Li; Jiao Licheng; Bai Bendu
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Paper Abstract

This paper introduces an approach for radar target recognition by the range profiles based on the combination of wavelet transform and the evolutionary neural network. The whole recognition process consist of tow main stages. The first stage is concerned with feature extraction where the goal is to find the small number of features form high feature space that retains all information needed for an accurate recognition. The second stage is concerned with classification of the patterns based on their reduced features after feature extraction. As the radar echo, i.e., the range profile is a nonstationary signal, Mallat algorithm is used to select and compress the features of the range profiles in the first stage. In the second stage, we use an evolutionary neural network as classifier, that is, construct a feed-forward neural network as classifier by using a hybrid evolutionary algorithm based on evolutionary programming. The experimental results show the whole target recognition system has simple structure and good generalization ability.

Paper Details

Date Published: 5 April 2000
PDF: 8 pages
Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); doi: 10.1117/12.381708
Show Author Affiliations
Ying Li, Xidian Univ. (China)
Jiao Licheng, Xidian Univ. (China)
Bai Bendu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 4056:
Wavelet Applications VII
Harold H. Szu; Martin Vetterli; William J. Campbell; James R. Buss, Editor(s)

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