Combining wavelet transform and the evolutionary neural network for radar target recognition

Ying Li; Jiao Licheng; Bai Bendu

doi:10.1117/12.381708

5 April 2000 Combining wavelet transform and the evolutionary neural network for radar target recognition

Ying Li, Jiao Licheng, Bai Bendu

Proceedings Volume 4056, Wavelet Applications VII; (2000) https://doi.org/10.1117/12.381708
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Ying Li, Jiao Licheng, and Bai Bendu "Combining wavelet transform and the evolutionary neural network for radar target recognition", Proc. SPIE 4056, Wavelet Applications VII, (5 April 2000); https://doi.org/10.1117/12.381708

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