Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 4
1105-1110

Research on radar action recognition method based on FT_SSIM and ICAGA_CNN in small sample scenes

Jiang Liubinga,b
Pan Boc
Wu Minyangc
Zhu Boqingc
Che Lia,b
a. School of Information & Communication, b. Key Laboratory of Wireless Broadband Communication & Signal Processing in Guangxi, c. School of Computer & Information Security, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2021.09.0378

Abstract

Aiming at the problems of poor training effect, over-fitting phenomenon in human action recognition based on small sample scenarios, slow convergence speed of traditional confrontation generation network, and high requirements for computer performance, this paper proposed solutions from data enhancement and hyperparameter optimization. Firstly, it built the AWR1243 radar data acquisition platform to preprocess the collected echo signals. Secondly, it used STFT for time-frequency analysis and the newly proposed FT_SSIM algorithm for data enhancement. Furthermore, it used the proposed ICAGA_CNN for classification and recognition, and compared the experiments with traditional data enhancement algorithms and hyperparameter optimization. In order to verify the proposed algorithm that has a certain generalization ability, the public KTH human body motion data set and the actual measurement data of radar were used for verification. Experimental results show that, on the one hand, the proposed algorithm effectively avoids the occurrence of over-fitting in small sample scenarios, reduced the requirements of traditional data enhancement on computer performance, and accelerated the speed of convergence. On the other hand, the proposed algorithm has better recognition accuracy, with an average recognition rate of 98.5%. This also shows that the proposed algorithm has a good performance in radar action recognition in small sample scenarios.

Foundation Support

国家自然科学基金资助项目(61561010)
广西创新驱动发展专项(桂科AA21077008)
广西重点研发计划资助项目(桂科AB18126003,AB18221016)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.09.0378
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 4
Section: Algorithm Research & Explore
Pages: 1105-1110
Serial Number: 1001-3695(2022)04-025-1105-06

Publish History

[2021-11-22] Accepted Paper
[2022-04-05] Printed Article

Cite This Article

蒋留兵, 潘波, 吴岷洋, 等. 基于FT_SSIM和ICAGA_CNN在小样本场景下雷达动作识别方法研究 [J]. 计算机应用研究, 2022, 39 (4): 1105-1110. (Jiang Liubing, Pan Bo, Wu Minyang, et al. Research on radar action recognition method based on FT_SSIM and ICAGA_CNN in small sample scenes [J]. Application Research of Computers, 2022, 39 (4): 1105-1110. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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