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Radar Signal Waveform Recognition Based on Convolutional Denoising Autoencoder

  • Zhaolei Liu
  • Xiaojie MaoEmail author
  • Zhian Deng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

To solve the problem of the low recognition rate of the existing methods at low signal-to-noise ratio (SNR), we propose a novel method of radar signal waveform recognition. In this method, we extract the time-frequency images (TFIs) of radar signals through Cohen class time frequency distribution. Then, we introduce convolutional denoising autoencoder (CDAE) to denoise and repairs the TFIs. Finally, we build a convolutional neural network (CNN) to identify the TFIs of radar signals. Simulation experiment shows that the proposed method can identify 12 kinds of radar signal waveforms, and the overall probability of successful recognition (PSR) is 95.4% when the SNR is −7 dB.

Keywords

Radar signal recognition Cohen class time frequency distribution Convolutional denoising autoencoder Convolutional neural network 

References

  1. 1.
    Latombe G, Granger E, Dilkes FA. Fast learning of grammar production probabilities in radar electronic support. IEEE Trans Aerosp Electron Syst. 2010;46(3):1262–89.CrossRefGoogle Scholar
  2. 2.
    Gupta M, Hareesh G, Mahla AK. Electronic warfare: issues and challenges for emitter classification. Defence Sci J. 2011;61(3):228–34.CrossRefGoogle Scholar
  3. 3.
    Zeng D, Zeng X, Cheng H, Tang B. Automatic modulation classification of radar signals using the Rihaczek distribution and Hough transform. IET Radar Sonar Navig. 2012;6(5):322–31.CrossRefGoogle Scholar
  4. 4.
    Zhou D, Wang X, Tian Y, Wang R. A novel radar signal recognition method based on a deep restricted Boltzmann machine; 2017Google Scholar
  5. 5.
    Zhang M, Diao M, Guo L. Convolutional neural networks for automatic cognitive radio waveform recognition. IEEE Access. 2017;5:11074–92.CrossRefGoogle Scholar
  6. 6.
    Jain V, Seung HS. Natural image denoising with convolutional networks. In: International conference on neural information processing systems; 2008. p. 769–76Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Nanjing Research Institute of Electronics TechnologyNanjingChina
  2. 2.College of Information and TelecommunicationHarbin Engineering UniversityHarbinChina

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