Volume 14 Issue 1
Jan.  2025
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Article Navigation >  Journal of Radars  > 2025  >  14(1): 117-134
YANG Xuan, WANG Ziying, ZHANG Li, et al. Noncontact multiperson respiratory detection method based on blind source separation[J]. Journal of Radars, 2025, 14(1): 117–134. doi: 10.12000/JR24115
Citation: YANG Xuan, WANG Ziying, ZHANG Li, et al. Noncontact multiperson respiratory detection method based on blind source separation[J]. Journal of Radars, 2025, 14(1): 117–134. doi: 10.12000/JR24115
shu

Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation

DOI: 10.12000/JR24115 CSTR: 32380.14.JR24115
  • 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 (62431013, 62301255, 62201259), Natural Science Foundation of Jiangsu Province (BK20220942, BK20220940), Fundamental Research Funds for the Central Universities (30923011026, 30923011006)
More Information
  • Corresponding author: ZHAO Heng, soniczhao@live.com; HONG Hong, hongnju@njust.edu.cn
  • Received Date: 2024-06-05
  • Rev Recd Date: 2024-08-21
    • Available Online: 2024-08-26
  • Publish Date: 2024-09-24
    Abstract
  • In recent years, there has been an increasing interest in respiratory monitoring in multiperson environments and simultaneous monitoring of the health status of multiple people. Among the algorithms developed for multiperson respiratory detection, blind source separation algorithms have attracted the attention of researchers because they do not require prior information and are less dependent on hardware performance. However, in the context of multiperson respiratory monitoring, the current blind source separation algorithm usually separates phase signals as the source signal. This article compares the distance dimension and phase signals under Frequency-modulated continuous-wave radar, calculates the approximate error associated with using the phase signal as the source signal, and verifies the separation effect through simulations. The distance dimension signal is better to use as the source signal. In addition, this article proposes a multiperson respiratory signal separation algorithm based on noncircular complex independent component analysis and analyzes the impact of different respiratory signal parameters on the separation effect. Simulation and experimental measurements show that the proposed method is suitable for detecting multiperson respiratory signals under controlled conditions and can accurately separate respiratory signals when the angle of the two targets to the radar is 9.46°.

     

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