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Electronics and Telecommunications Research Institute (한국전자통신연구원)
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Automatic modulation classification of noise-like radar intrapulse signals using cascade classifier

  • Meng, Xianpeng (Department of Electronic and Optical Engineering, Army Engineering University) ;
  • Shang, Chaoxuan (Department of Electronic and Optical Engineering, Army Engineering University) ;
  • Dong, Jian (Department of Electronic and Optical Engineering, Army Engineering University) ;
  • Fu, Xiongjun (School of Information and Electronics, Beijing Institute of Technology) ;
  • Lang, Ping (School of Information and Electronics, Beijing Institute of Technology)
  • Received : 2020.09.04
  • Accepted : 2021.03.11
  • Published : 2021.12.01
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Abstract

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.

Keywords

Acknowledgement

This work was supported by the Equipment Development Department of People's Republic of China Central Military Commission under Grants 1415010503 and 61404130211.

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