Volume 6 Issue 2
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Article Navigation >  Journal of Radars  > 2017  >  6(2): 149-156
Zhao Feixiang, Liu Yongxiang, Huo Kai. Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder[J]. Journal of Radars, 2017, 6(2): 149-156. doi: 10.12000/JR16151
Citation: Zhao Feixiang, Liu Yongxiang, Huo Kai. Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder[J]. Journal of Radars, 2017, 6(2): 149-156. doi: 10.12000/JR16151
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Radar Target Recognition Based on Stacked Denoising Sparse Autoencoder

doi: 10.12000/JR16151

  • College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China

Funds:  The National Natural Science Foundation of China (61422114), The Natural Science Fund for Distinguished Young Scholars of Hunan Province (2015JJ1003)
  • Received Date: 2016-12-22
  • Rev Recd Date: 2017-01-10
    • Available Online: 2017-03-13
  • Publish Date: 2017-04-28
    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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    • References(18)
  • [1]
    吴剑旗, 田西兰. 一种基于半监督学习的窄带雷达目标识别系统[J]. 中国电子科学研究院学报, 2015, 10(1): 49–53. http://www.cnki.com.cn/Article/CJFDTOTAL-KJPL201501008.htm

    Wu Jian-qi and Tian Xi-lan. A narrow-band radar target recognition system based on semi-supervised learning[J].Journal of CAEIT, 2015, 10(1): 49–53. http://www.cnki.com.cn/Article/CJFDTOTAL-KJPL201501008.htm
    [2]
    张平定, 孙佳佳, 童创明, 等. 弹道中段目标雷达综合识别研究[J]. 微波学报, 2015, 31(2): 20–23. http://cdmd.cnki.com.cn/Article/CDMD-90002-1012020687.htm

    Zhang Ping-ding, Sun Jia-jia, Tong Chuang-ming, et al. Integrated target recognition of ballistic midcourse target[J].Journal of Microwaves, 2015, 31(2): 20–23. http://cdmd.cnki.com.cn/Article/CDMD-90002-1012020687.htm
    [3]
    曹伟, 周智敏, 周辉, 等. 基于多维特征及BP网络的高分辨雷达目标识别[J]. 计算机工程与应用, 2013, 49(8): 213–216. http://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201308052.htm

    Cao Wei, Zhou Zhi-min, Zhou Hui, et al. High resolution radar target recognition based on multi-dimensional feature vector and BP network[J].Computer Engineering and Applications, 2013, 49(8): 213–216. http://www.cnki.com.cn/Article/CJFDTOTAL-JSGG201308052.htm
    [4]
    Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J].Science, 2006, 313(5786): 504–507. doi: 10.1126/science.1127647
    [5]
    Lecun Y, Bengio Y, and Hinton G. Deep learning[J].Nature, 2015, 521(7553): 436–444. doi: 10.1038/nature14539
    [6]
    尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015, 41(1): 48–57. http://youxian.cnki.com.cn/yxdetail.aspx?filename=DSWZ201706161&dbname=CJFDPREN

    Yin Bao-cai, Wang Wen-tong, and Wang Li-chun. Review of deep learning[J].Journal of Beijing University of Technology, 2015, 41(1): 48–57. http://youxian.cnki.com.cn/yxdetail.aspx?filename=DSWZ201706161&dbname=CJFDPREN
    [7]
    丁军, 刘宏伟, 陈渤, 等. 相似性约束的深度置信网络在SAR图像目标识别的应用[J]. 电子与信息学报, 2016, 38(1): 97–103. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201601013.htm

    Ding Jun, Liu Hong-wei, Chen Bo, et al. Similarity constrained deep belief networks with application to SAR image target recognition[J].Journal of Electronics &Information Technology, 2016, 38(1): 97–103. http://www.cnki.com.cn/Article/CJFDTOTAL-DZYX201601013.htm
    [8]
    田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. http://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201603012.htm

    Tian Zhuang-zhuang, Zhan Rong-hui, Hu Jie-min, et al. SAR ATR based on convolutional neural network[J].Journal of Radars, 2016, 5(3): 320–325. http://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201603012.htm
    [9]
    Jiang Xiao-juan, Zhang Ying-hua, Zhang Wen-sheng, et al. A novel sparse auto-encoder for deep unsupervised learning[C]. 2013 Sixth International Conference on Advanced Computational Intelligence, Hangzhou, 2013: 256–261.
    [10]
    Feng Bo, Chen Bo, and Liu Hong-wei. Radar HRRP target recognition with deep networks[J].Pattern Recognition, 2017, 61: 379–393.
    [11]
    张成刚, 姜静清. 一种稀疏降噪自编码神经网络研究[J]. 内蒙古民族大学学报(自然科学版), 2016, 31(1): 21–25. http://www.cnki.com.cn/Article/CJFDTOTAL-NMMS201601007.htm

    Zhang Cheng-gang and Jiang Jing-qing. Study on sparse De-noising Auto-Encoder neural network[J].Journal of Inner Mongolia University for Nationalities, 2016, 31(1): 21–25. http://www.cnki.com.cn/Article/CJFDTOTAL-NMMS201601007.htm
    [12]
    Meng Ling-heng, Ding Shi-fei, and Xue Yu. Research on denoising sparse autoencoder[J].International Journal of Machine Learning and Cybernetics, 2016. DOI: 10.1007/s13042-016-0550-y.
    [13]
    Kumar V, Nandi G C, and Kala R. Static hand gesture recognition using stacked denoising sparse autoencoders[C]. 2014 Seventh International Conference on Contemporary Computing, Noida, 2014: 99–104.
    [14]
    Sankaran A, Pandey P, Vatsa M, et al. On latent fingerprint minutiae extraction using stacked denoising sparse AutoEncoders[C]. IEEE International Joint Conference on Biometrics, Clearwater, FL, 2014: 1–7.
    [15]
    Jose Dolz, Nacim Betrouni, Mathilde Quidet, et al. Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: A clinical study[J].Computerized Medical Imaging and Graphics, 2016, 52: 8–18. doi: 10.1016/j.compmedimag.2016.03.003
    [16]
    Michael A Nielsen. Neural Networks and Deep Learning[M]. Determination Press, 2015.
    [17]
    Sun Wen-jun, Shao Si-yu, Zhao Rui, et al. A sparse auto-encoder-based deep neural network approach for induction motor faults classification[J].Measurement, 2016, 89: 171–178. doi: 10.1016/j.measurement.2016.04.007
    [18]
    Xing Chen, Ma Li, and Yang Xiao-quan. Stacked denoise autoencoder based feature extraction and classification for hyperspectral images[J].Journal of Sensors, 2016. DOI: 10.1155/2016/3632943.
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