In view of the problem that radar target recognition methods based on traditional high resolution range profile (HRRP) are susceptible to noise, an HRRP radar target recognition method employing time-frequency analysis and deep learning is proposed. First, low signal-to-noise ratio HRRP data is processed, and gains an improved signal-to-noise ratio by using a generative model which uses deep convolutional generative adversarial network (DCGAN) and constrained naive least squares generative adversarial network (CNLSGAN) proposed in this paper. Second, the processed data is processed with short-time Fourier transform (STFT) and wavelet transform (wavelet transform, WT) respectively to obtain two-dimensional time-frequency data. Finally, the obtained two-dimensional data is recognized by convolutional neural network (CNN). Experimental results show that the proposed CN-LSGAN performs better in improving signal-to-noise ratio compared to DCGAN, and WT can obtain HRRP feature information more efficiently than STFT. Therefore, the HRRP radar target recognition method based on CN-LSGAN, WT and CNN has higher recognition ability.
NIE Jianghua, XIAO Yongsheng, HUANG Lizhen, HE Fengshou
. High Resolution Range Profile Radar Target Recognition Based on Time-Frequency Analysis and Deep Learning[J]. Journal of Applied Sciences, 2022
, 40(6)
: 973
-983
.
DOI: 10.3969/j.issn.0255-8297.2022.06.008
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