Volume 13 Issue 1
Feb.  2024
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Article Navigation >  Journal of Radars  > 2024  >  13(1): 68-86
YANG Xiaopeng, GAO Weicheng, and QU Xiaodong. Human anomalous gait termination recognition via through-the-wall radar based on micro-Doppler corner features and Non-Local mechanism[J]. Journal of Radars, 2024, 13(1): 68–86. doi: 10.12000/JR23181
Citation: YANG Xiaopeng, GAO Weicheng, and QU Xiaodong. Human anomalous gait termination recognition via through-the-wall radar based on micro-Doppler corner features and Non-Local mechanism[J]. Journal of Radars, 2024, 13(1): 68–86. doi: 10.12000/JR23181
shu

Human Anomalous Gait Termination Recognition via Through-the-wall Radar Based on Micro-Doppler Corner Features and Non-Local Mechanism

doi: 10.12000/JR23181

  • School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China

Funds:  The National Natural Science Foundation of China (61860206012), Beijing Institute of Technology Research Fund Program for Young Scholars
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  • Corresponding author: QU Xiaodong, xdqu@bit.edu.cn
  • Received Date: 2023-10-04
  • Rev Recd Date: 2023-11-22
    • Available Online: 2023-12-05
  • Publish Date: 2023-12-15
    Abstract
  • Through-the-wall radar can penetrate walls and realize indoor human target detection. Deep learning is commonly used to extract the micro-Doppler signature of a target, which can be used to effectively identify human activities behind obstacles. However, the test accuracy of the deep-learning-based recognition methods is low with poor generalization ability when different testers are invited to generate the training set and test set. Therefore, this study proposes a method for recognition of anomalous human gait termination based on micro-Doppler corner features and Non-Local mechanism. In this method, Harris and Moravec detectors are utilized to extract the corner features of the radar image, and the corner feature dataset is established in this manner. Thereafter, multilink parallel convolutions and the Non-Local mechanism are utilized to construct the global contextual information extraction network to learn the global distribution characteristics of the image pixels. The semantic feature maps are generated by repeating four times the global contextual information extraction network. Finally, the probabilities of human activities are predicted using a multilayer perceptron. The numerical simulation and experimental results demonstrate that the proposed method can effectively identify such abnormal gait termination activities as sitting, lying down, and falling, among others, which occur in the process of indoor human walking, and successfully control the generalization accuracy error to be no more than $ 6.4\% $ under the premise of increasing the recognition accuracy and robustness.

     

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