Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder
Zhao Feixiang Liu Yongxiang* Huo Kai
(College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
Abstract 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.
Key words ： Target recognition
Deep learning
Stacked denoising sparse autoencoder
Received: 2016-12-22;
Published: 2017-03-13
Fund: The National Natural Science Foundation of China (61422114), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2015JJ1003)
Cite this article:
Zhao Feixiang,Liu Yongxiang,Huo Kai. Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder[J]. JOURNAL OF RADARS, 2017, 6(2): 149-156.
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2017, Vol. 6 
