Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (7): 877-885.doi: 10.16183/j.cnki.jsjtu.2021.209

• Electronic Information and Electrical Engineering • Previous Articles     Next Articles

Radar Signal Recognition Based on Dual Channel Convolutional Neural Network

QUAN Daying(), CHEN Yun, TANG Zeyu, LI Shitong, WANG Xiaofeng, JIN Xiaoping   

  1. College of Information Engineering, China Jiliang University, Hangzhou 310018, China
  • Received:2021-06-15 Online:2022-07-28 Published:2022-08-16


In order to solve the problems of difficult feature extraction and low recognition rate of radar signal at low signal-to-noise ratios, a dual channel convolutional neural network model based on Choi-Williams distribution (CWD) and multisynchrosqueezing transform (MSST) is proposed, which obtains two-dimensional time-frequency images by CWD and MSST time-frequency analyses on radar signals. Respectively, the time-frequency images are preprocessed and sequencely fed to a dual channel convolutional neural network for deep feature extraction. Finally, the features acquired by the two channels are fused, and the radar signal is classified and recognized through the convolutional neural network classifier. The simulation results show that when the signal-to-noise ratio is -10 dB, the overall recognition accuracy can reach above 96%, which is excellent at low signal-to-noise ratios.

Key words: low signal-to-noise ratios, Choi-Williams distribution (CWD), multisynchrosqueezing transform (MSST), dual-channel convolutional neural network

CLC Number: