Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and...

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Bibliographic Details
Published in:Journal of radars = Lei da xue bao Vol. 6; no. 2; pp. 149 - 156
Main Authors: Zhao Feixiang, Liu Yongxiang, Huo Kai
Format: Journal Article
Language:Chinese
English
Published: China Science Publishing & Media Ltd. (CSPM) 28.04.2017
Subjects:
Deep learning
Stacked denoising sparse autoencoder
Target recognition
ISSN:2095-283X, 2095-283X
Online Access:Get full text
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Summary:Feature extraction is a key step in radar target recognition. The quality of the extracted features determines the performance of target recognition. However, obtaining the deep nature of the data is difficult using the traditional method. The autoencoder can learn features by making use of data and can obtain feature expressions at different levels of data. To eliminate the influence of noise, the method of radar target recognition based on stacked denoising sparse autoencoder is proposed in this paper. This method can extract features directly and efficiently by setting different hidden layers and numbers of iterations. Experimental results show that the proposed method is superior to the K-nearest neighbor method and the traditional stacked autoencoder.
ISSN:2095-283X
2095-283X
DOI:10.12000/JR16151