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ZHAO Xingpeng, XU Hang, CHANG Pengfa, et al. A semisupervised radar-based human action recognition method via multidomain collaborative training[J]. Journal of Radars, in press. doi: 10.12000/JR25223
Citation: ZHAO Xingpeng, XU Hang, CHANG Pengfa, et al. A semisupervised radar-based human action recognition method via multidomain collaborative training[J]. Journal of Radars, in press. doi: 10.12000/JR25223
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A Semisupervised Radar-Based Human Action Recognition Method via Multidomain Collaborative Training

DOI: 10.12000/JR25223 CSTR: 32380.14.JR25223
  • 1.

    Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Jinzhong 030600, China

  • 2.

    College of Physics and Optoelectronics, Taiyuan University of Technology, Jinzhong 030600, China

  • 3.

    College of Electronic Information Engineering, Taiyuan University of Technology, Jinzhong 030600, China

Funds:  The National Cryptologic Science Fund of China under Grant (2025NCSF02059), The National Natural Science Foundation of China (42174175, 62105233), The Fundamental Research Program of Shanxi Province (202203021221090)
More Information
  • Corresponding author: XU Hang, xuhang@tyut.edu.cn
  • Received Date: 2025-11-04
    Available Online: 2026-03-05
    Abstract
  • To address the problem of insufficient labeled data in radar-based human action recognition, a semisupervised learning method based on multidomain collaborative training is proposed herein. This method fuses action features from the slow time–range, slow time–Doppler frequency, and range–Doppler frequency domains to construct a decision-level ensemble framework. An interdomain consistency evaluation mechanism is employed to dynamically adjust the contribution of each domain in ensemble prediction. Furthermore, a stratified confidence dynamic pseudo-labeling strategy is designed to balance pseudo-label quality and utilization rate through multilevel quality assessment and dynamic threshold calibration. In addition, a feature alignment constraint mechanism is introduced, where fast principal component analysis is utilized to extract the principal components of multidomain features. This mechanism guides the deep network to learn compact feature representations and enhances model discriminability. On the through-wall human action dataset based on a random code radar, an average recognition accuracy of (93.6±1.6)% is achieved with 5% labeled data; meanwhile, the corresponding accuracy on the indoor human action dataset based on a frequency modulated continuous-wave (FMCW) radar is (91.3±1.9)%. The proposed method outperforms supervised learning methods, including the use of Bi-LSTM, LH-ViT, and MFAFN, as well as semisupervised learning methods, including the use of FixMatch, C-TGAN, MF-Match, and LW-HGR. Experimental results demonstrate that the proposed method exhibits stable performance across two different radar systems (random code and FMCW radars) and two detection scenarios (through-wall and indoor scenarios), validating its cross-system and cross-scenario adaptability. Finally, the semisupervised learning model based on multidomain collaborative training has 1.30M parameters, requires 26.16M floating-point operations, and exhibits a size of 5.01 MB, demonstrating high computational efficiency.

     

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