Radar signal recognition based on time-frequency feature extraction and residual neural network

被引：0
|
作者：
Xie C. [1 ]
Zhang L. [1 ]
Zhong Z. [2 ]
机构：
[1] Department of Information Fusion, Naval Aviation University, Yantai
[2] School of Basis Aviation, Naval Aviation University, Yantai
来源：
Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2021年 / 43卷 / 04期
关键词：
Chirp-based decomposition; Fractional Fourier transform; Radar signal identification; Residual neural network; Zernike moment;
D O I：
10.12305/j.issn.1001-506X.2021.04.08
中图分类号：
学科分类号：
摘要：
Aiming at the problem of low recognition rate of radar signal pulse modulation type under low signal to noise ratio(SNR), a radar signal recognition algorithm based on time-frequency feature extraction and residual neural network is proposed. The time-frequency feature extraction firstly performs chirp-based decomposition of the signal through the fractional Fourier transform, classifies the signal according to different combinations of chirp-based carrier frequency and frequency modulation, and sets the corresponding classification feature parameters. Then, the pseudo Wigner-Ville time-frequency distribution of the signal is calculated and Zernike moments is extracted. The above-mentioned characteristic parameters form a signal characteristic vector, and a residual neural network classifier is used to realize radar signal recognition. Simulation results show that the recognition accuracy can reach more than 93% when SNR is under -2 dB. At the same time, the robustness is well verified, and the algorithm complexity can meet the actual requirements. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
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页码：917 / 926
页数：9
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