Radar target recognition method based on one-dimensional convolutional neural network

Mingming Zhu; Zongxin Liu; Jin Jiang; Xingwen Qiao; Zhiyong Zhao

doi:10.1117/12.3038669

22 July 2024 Radar target recognition method based on one-dimensional convolutional neural network

Mingming Zhu, Zongxin Liu, Jin Jiang, Xingwen Qiao, Zhiyong Zhao

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 132220S (2024) https://doi.org/10.1117/12.3038669
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

Radar Cross Section (RCS) is the most important amplitude characteristic of target electromagnetic scattering. Aiming at the low efficiency of low-resolution radar target recognition, a radar target recognition method based on one-dimensional convolutional neural network is proposed. According to the variation rule of RCS data sequence, the target RCS data sequence is generated by simulation first, and then the feature extraction of the target RCS data sequence is carried out by convolutional layer and pooling layer. Finally, the target RCS data sequence is recognized by full connection layer and Softmax layer. The experimental results show that the proposed method can classify and recognize the radar target and has good recognition performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mingming Zhu, Zongxin Liu, Jin Jiang, Xingwen Qiao, and Zhiyong Zhao "Radar target recognition method based on one-dimensional convolutional neural network", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 132220S (22 July 2024); https://doi.org/10.1117/12.3038669

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