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Noncontact Multiperson Respiratory Detection Method Based on Blind Source Separation
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摘要: 近年来，人们越来越关注多人环境下的呼吸监测，以及如何同时监测多人的健康状态。在多人呼吸检测的算法中，盲源分离算法因其无需先验信息并且对硬件性能依赖性较小而备受研究者关注。然而，在多人呼吸监测场景中，目前的盲源分离算法通常将相位信号作为源信号进行分离，该文引入FMCW雷达下距离维信号和相位信号的对比，推导出相位信号作为源信号存在近似误差，并通过仿真验证距离维信号作为源信号时分离效果更好。另外，该文提出了基于非圆复数独立成分分析的多人呼吸信号分离算法，分析了不同呼吸信号参数对分离效果的影响，仿真和实测实验表明，所提出的方法适用于天线个数不小于目标个数时多人呼吸信号的检测，并且在目标角度差为9.46°时，也能够准确分离呼吸信号。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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图 2 多人呼吸分离的总体原理图
Figure 2. Overall schematic diagram for multi-person respiration detection
图 3 两天线两目标复数盲源分离效果图
Figure 3. Effect diagram of complex blind source separation with two antennas
图 4 呼吸信号及呼吸参考信号对比
Figure 4. Comparison of the respiratory signals and respiratory reference signals
图 5 基于相位信号恢复的呼吸信号及呼吸参考信号对比
Figure 5. Respiratory signal based on phase signal recovery
图 6 3天线3目标恢复的呼吸信号及呼吸参考信号对比
Figure 6. Comparison of the respiratory signals and respiratory reference signals recovered from three antennas and three subjects
图 7 不同信噪比下信号重构的误差
Figure 7. Error of signal reconstruction under different signal-to-noise ratios
图 8 不同呼吸参数下的波形重构误差
Figure 8. Error of signal reconstruction under different respiratory parameters
图 11 1 m处双人正对雷达时分离出的呼吸波形以及参考波形
Figure 11. Respiratory waveforms and reference waveforms separated at 1 m when two individuals face the radar directly
图 12 1 m处双人正对雷达时分离前后的星座图
Figure 12. Constellation diagrams before and after separation when two individuals face the radar directly at 1 m
图 13 1m处双人侧对雷达时分离出的呼吸波形以及参考波形
Figure 13. Respiratory waveforms and reference waveforms separated at 1m when two individuals face the radar sideways
图 14 1m处双人背对雷达时分离出的呼吸波形以及参考波形
Figure 14. Respiratory waveforms and reference waveforms separated at 1m when two individuals face away from the radar
图 15 呼吸异常时雷达分离出的呼吸波形以及参考波形
Figure 15. Respiratory waveforms and reference waveforms separated by radar during abnormal respiration
图 16 10 min内雷达得到的两个测试者的呼吸率以及参考呼吸率
Figure 16. The respiratory rates of two testers obtained by radar within 10 minutes, along with the reference respiratory rate
图 17 两人距离雷达1.8 m场景示意图
Figure 17. Illustration of two people at a distance of 1.8 meters from the radar
图 18 两人距离雷达1.8 m时分离出两人的雷达呼吸信号以及呼吸参考信号
Figure 18. Radar respiratory signals of two persons separated when the distance to the radar is 1.8 m, and the reference respiratory signal
图 19 两人呼吸频率一致时分离出两人的雷达呼吸信号以及呼吸参考信号
Figure 19. Radar respiratory signals of two persons separated when their respiratory frequencies are consistent, and the reference respiratory signal
图 20 两人距离雷达1.8 m和2 m场景示意图
Figure 20. Illustration of two people at a distance of 1.8 meters and 2 meters from the radar
图 21 1.8 m处两个目标的呼吸波形及角度
Figure 21. Respiratory waveforms and angles of two subjects at 1.8 m
图 22 2 m处两个目标的呼吸波形及角度
Figure 22. Respiratory waveforms and angles of two subjects at 2 m
1 多人呼吸分离算法流程
1. Steps of complex multi-person blind source separation algorithm
输入：N个天线的距离维信号X 输出：分离后的N个目标的距离维信号 初始化：初始化解混矩阵${{\boldsymbol{W}}^{\text{H}}} = \left[ {{{\boldsymbol{w}}_1} {{\boldsymbol{w}}_2} \cdots {{\boldsymbol{w}}_N}} \right] = {\boldsymbol{I}}$ 1 通过式(18)对观测数据X进行白化，得到Z 2 令$k = 1$ 3 使用式(25)对$ {{\boldsymbol{w}}_i} $进行更新，直到满足收敛条件 4 $k = k + 1$，使用步骤3完成下一个列向量的估计 5 完成所有分量分离，并使用式(26)进行正交变换 6 得到混合矩阵的估计$ \tilde {\boldsymbol{A}} = {{\boldsymbol{V}}^{ - 1}}{\boldsymbol{W}} $，以及源信号的估计
$ {\boldsymbol{Y}} = {{\boldsymbol{W}}^{\text{H}}}{\boldsymbol{Z}} $7 按照式(28)和式(29)去除相位模糊 
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表 1 仿真实验参数
Table 1. Simulation experiment parameters for two antennas and two targets
参数 数值 载频 24 GHz 天线阵元个数 2 天线间距 $\lambda /2$
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表 2 不同目标个数、不同天线数量时峭度和
Table 2. Sum of kurtosis for different number of targets, different number of antennas
天线个数 1个目标 2个目标 3个目标 1 1.00 1.50 1.75 2 1.00 2.35 2.77 3 1.00 2.35 3.48
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表 3 FMCW雷达硬件参数设置
Table 3. Hardware parameters setting for FMCW radar
参数 数值 起始频率 24 GHz 带宽 1 GHz Chirp采样点个数 1024 ADC采样速率 2.5 MHz Chirp重复周期 10 ms
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