Wavelet Autoencoder for Radar HRRP Target Recognition with Recurrent Neural Network

被引：1
|
作者：
Zhang, Mengjiao [1 ]
Chen, Bo [1 ]
机构：
[1] Xidian Univ, Natl Lab Radar Signal Proc, Xian, Peoples R China
来源：
INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING | 2018年 / 11266卷
关键词：
High resolution range profiles (HRRP); Recurrent neural network (RNN); Radar automatic target recognition (RATR); Overcomplete bases; Joint training; Wavelet; Autoencoder (AE); MODEL;
D O I：
10.1007/978-3-030-02698-1_23
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A Wavelet Autoencoder model with Recurrent Neural Network (WaveletAE with RNN) is developed for radar automatic target recognition (RATR), with an encoder-decoder layer, in which the weights of decoder are fixed as a set of overcomplete bases derived from mother wavelet. Imposing an sparsity constraint on the hidden units of encoder-decoder layer, interesting structure in the data is discovered, and superior recognition performance is achieved on the measured High Resolution Range Profiles (HRRP) data, showing the effectiveness of the proposed model. Specific results are represented in our experiments.
引用
收藏
页码：262 / 275
页数：14
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