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      Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

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          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.

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          Author and article information

          Journal
          Journal of Radars
          Chinese Academy of Sciences
          01 April 2017
          : 6
          : 2
          : 149-156
          Affiliations
          [1 ] (College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China)
          Article
          1c2d823be05147f8a13145c2075c9645
          10.12000/JR16151

          This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

          Categories
          Technology (General)
          T1-995

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