Volume 14 Issue 1
Jan.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(1): 151-167
DING Chuanwei, LIU Zhilin, ZHANG Li, et al. Tangential human posture recognition with sequential images based on MIMO radar[J]. Journal of Radars, 2025, 14(1): 151–167. doi: 10.12000/JR24116
Citation: DING Chuanwei, LIU Zhilin, ZHANG Li, et al. Tangential human posture recognition with sequential images based on MIMO radar[J]. Journal of Radars, 2025, 14(1): 151–167. doi: 10.12000/JR24116
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Tangential Human Posture Recognition with Sequential Images Based on MIMO Radar

DOI: 10.12000/JR24116 CSTR: 32380.14.JR24116
  • 1.

    Nanjing University of Science and Technology, Nanjing 210000, China

  • 2.

    Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China

Funds:  The National Natural Science Foundation of China (62201259, 62301255), Fundamental Research Funds for the Central Universities (30923011006, 30923011026)
More Information
  • Corresponding author: HONG Hong, hongnju@njust.edu.cn; ZHU Xiaohua, zxh@njust.edu.cn
  • Received Date: 2024-06-05
  • Rev Recd Date: 2024-07-18
    • Available Online: 2024-07-25
  • Publish Date: 2024-08-16
    Abstract
  • Recent research on radar-based human activity recognition has typically focused on activities that move toward or away from radar in radial directions. Conventional Doppler-based methods can barely describe the true characteristics of nonradial activities, especially static postures or tangential activities, resulting in a considerable decline in recognition performance. To address this issue, a method for recognizing tangential human postures based on sequential images of a Multiple-Input Multiple-Output (MIMO) radar system is proposed. A time sequence of high-quality images is achieved to describe the structure of the human body and corresponding dynamic changes, where spatial and temporal features are extracted to enhance the recognition performance. First, a Constant False Alarm Rate (CFAR) algorithm is applied to locate the human target. A sliding window along the slow time axis is then utilized to divide the received signal into sequential frames. Next, a fast Fourier transform and the 2D Capon algorithm are performed on each frame to estimate range, pitch angle, and azimuth angle information, which are fused to create a tangential posture image. They are connected to form a time sequence of tangential posture images. To improve image quality, a modified joint multidomain adaptive threshold-based denoising algorithm is applied to improve the image quality by suppressing noises and enhancing human body outline and structure. Finally, a Spatio-Temporal-Convolution Long Short Term Memory (ST-ConvLSTM) network is designed to process the sequential images. In particular, the ConvLSTM cell is used to extract continuous image features by combining convolution operation with the LSTM cell. Moreover, spatial and temporal attention modules are utilized to emphasize intraframe and interframe focus for improving recognition performance. Extensive experiments show that our proposed method can achieve an accuracy rate of 96.9% in classifying eight typical tangential human postures, demonstrating its feasibility and superiority in tangential human posture recognition.

     

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