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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Vydáno v:	Journal of radars = Lei da xue bao Ročník 6; číslo 2; s. 149 - 156
Hlavní autoři:	Zhao Feixiang, Liu Yongxiang, Huo Kai
Médium:	Journal Article
Jazyk:	čínština angličtina
Vydáno:	China Science Publishing & Media Ltd. (CSPM) 28.04.2017
Témata:	Deep learning Stacked denoising sparse autoencoder Target recognition
ISSN:	2095-283X, 2095-283X
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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