Research on radar signal source identification method based on deep learning

Shuyao Li; Hongmei Huang; Qingchao Zhu; Rui Deng; Yanli Fu

doi:10.1117/12.2674662

6 April 2023 Research on radar signal source identification method based on deep learning

Shuyao Li, Hongmei Huang, Qingchao Zhu, Rui Deng, Yanli Fu

Proceedings Volume 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022); 126150S (2023) https://doi.org/10.1117/12.2674662
Event: International Conference on Signal Processing and Communication Technology (SPCT 2022), 2022, Harbin, China

Abstract

Aiming at the problem that it is difficult to identify the complex radar signal under the condition of low signal-to-noise ratio using the traditional pulse-to-pulse parameters, a method using deep learning model to assist training and identify radar radiation sources is proposed. Firstly, the time-frequency image of radar signal is generated as training set 1 by the method of time-frequency analysis. Then, using the sample learning ability of the deep convolutional generative adversarial network, the time-frequency images are generated as training set 2 on the basis of training set 1. Compared with 1, training set 2 has the effect of denoising and data enhancement. Finally, the training set 2 is used to assist the training of the visual geometry group on the training set 1 to identify the radar radiation source. Simulation experiments are carried out on five common radar signals, and the experimental results verify the effectiveness of the method.

Citation Download Citation

Shuyao Li, Hongmei Huang, Qingchao Zhu, Rui Deng, and Yanli Fu "Research on radar signal source identification method based on deep learning", Proc. SPIE 12615, International Conference on Signal Processing and Communication Technology (SPCT 2022), 126150S (6 April 2023); https://doi.org/10.1117/12.2674662

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