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摘要: 在无感雷达体征监测中,与CW (Continuous wave)相比,调频式雷达(如FMCW和UWB)能实现对目标与杂波在距离上的有效区分。通过距离傅里叶变换,可以从不同距离区间提取出准静态目标的心跳和呼吸信号,从而提高监测精度。在已有研究中被广泛使用的距离FFT存在一些缺陷:首先,当受试者的呼吸幅度过大,胸腔反射面可能会跨越距离仓的边界,从而影响信号的完整性。其次,受试者的呼吸运动会对生理信号造成幅度上的调制,不利于体征信号的波形恢复。基于上述原因该文提出了基于距离抽头重构和动态解调的算法架构,针对上述两种情况,在仿真和实验中对算法性能进行了评估。仿真分析表明发生跨距离仓的信号经过所提出算法处理后,SNR提升了17±5 dB。此外,实验通过获取8名受试者的DHD (Doppler Heartbeat Diagram)信号,定量分析了DHD信号与BCG (Ballistocardiogram)的一致性,DHD信号中心跳间隔相对于BCG信号的心跳间隔的均方根误差(RMSE)为21.58±13.26 ms (3.40%±2.08%)。Abstract: In non-inductive radar vital sign monitoring, frequency-modulated radars (such as frequency modulated continuous wave and ultra wideband) are more effective than continuous wave radars at distinguishing targets from clutter in terms of distance. Using range Fourier transform, the heartbeat and breathing signals can be extracted from quasi-static targets across various distance intervals, thereby improving monitoring accuracy. However, the commonly used range fast Fourier transform presents certain limitations: The breathing amplitude of the subject may cross the range bin boundary, compromising signal integrity, while breathing movements can cause amplitude modulation of physiological signals, hindering waveform recovery. To address these reasons, we propose an algorithm architecture featuring range tap reconstruction and dynamic demodulation. We tested the algorithm performance in simulations and experiments for the cross range bin cases. Simulation results indicate that processing signals crossing range bins with our algorithm improves the signal-to-noise ratio by 17 ± 5 dB. In addition, experiments recorded Doppler heartbeat diagram (DHD) signals from eight subjects, comparing the consistency between the DHD signals and the ballistocardiogram. The root means square error of the C–C interval in the DHD signal relative to the J–J interval in the BCG signal was 21.58 ± 13.26 ms (3.40% ± 2.08%).
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图 2 距离FFT引发的幅度信号幅度调制以及跨距离仓的问题 (a)距离仓10和距离仓11 对目标的幅度响应;(b) 距离仓10和距离仓11 对目标的相位响应;(c)当目标运动的中心点分别位于距离仓10.5和距离仓10时幅度调制系数与运动幅度的关系;(d)当目标运动的中心点分别位于距离仓10.5 且位移达到一个距离仓宽度时的星座图;(e) 当目标运动的中心点分别位于距离仓10 且位移达到一个距离仓宽度时的星座图。
Figure 2. Amplitude modulation of the signal caused by range FFT and the problem of spanning range bins (a) Amplitude response of range bin 10 and range bin 11; (b) Phase response of range bin 10 and range bin 11; (c) Relationship between the amplitude modulation coefficient and the motion amplitude when the center point of the target motion is located in the range bin 10.5 and the range bin 10 respectively; (d) Constellation diagram when the center point of the target motion is located in the range bin 10.5 and the displacement reaches the width of one range bin; (e) Constellation diagram when the center point of the target motion is located in the range bin 10 and the displacement reaches the width of one range bin.
图 4 幅度调制信号经过不同解调算法提取的DCD: (a)幅度调制的生理复信号;(b) 经过反正切解调的DCD信号与参考信号; (c) 经过DACM解调后的DCD信号与参考信号。
Figure 4. DCD extracted from amplitude modulated signals using different demodulation algorithms: (a) Amplitude modulated physiological complex signal; (b) DCD signal and reference signal after arctan demodulation and the reference; (c) DCD signal and reference signal after DACM demodulation and the reference.
图 8 实验设置框图与实验设置(a)实验设置框图,(b)实验设置照片;(c)雷达传感器通过数据采集板连接到PC并通过无线网获取时间戳;(d)数据采集器通过ADC同步采集ECG和BCG并通过无线网获取时间戳。
Figure 8. Block diagram of the experimental setup and the experimental setup (a) Block diagram of the experimental setup, (b) Photo of the experimental setup, (c) The radar sensor is connected to the PC through the data acquisition board and obtains timestamps through the wireless network; (d) The data collector synchronously collects ECG and BCG through ADC and obtains timestamps through the wireless network.
图 9 仿真结果(a) 8种不同情况下通过距离FFT以及arctan解调后得到的信号的频谱图;(b) 对应的数据经过本文算法处理之后得到的信号的频谱图; (c) 距离FFT得到信号的8~16Hz频谱细节;(d) 本文算法获取信号的8~16 Hz频谱细节。
Figure 9. Simulation results (a) Spectrum diagram of the signal obtained by range FFT and arctan demodulation in 8 different cases; (b) Spectrum diagram of the signal obtained after the corresponding data is processed by the algorithm in this paper; (c) 8~16 Hz spectrum details of the signal obtained by range FFT; (d) 8~16 Hz spectrum details of the signal obtained by the proposed algorithm.
图 10 (a)雷达距离-时间图与距离FFT的增益;(d)雷达距离-时间图与本文算法得到的增益;(b) 距离仓31提取信号的星座图;(c) 距离仓31提取信号的相位图;(e) 通过距离仓重构提取信号的星座图;(f) 通过距离仓重构提取信号的相位。
Figure 10. (a)(d) Range-time heatmap, (b) IQ plot of signal from range bin 31, (c) phase of signal from range bin 31, (e) IQ plot of signal via Range tapper refactoring, (f) phase of signal via Range tapper refactoring.
图 12 DHD信号提取过程(a) 在算法处理之前和之后的生理复信号。(b)在算法处理之前和之后解调得到的DCD信号。(c) 从DCD信号中提取的DRD和DHD信号。(d) 用通带为7~16 Hz滤波器滤波后,DHD信号和同步采集的BCG信号之间的细节比较。
Figure 12. DHD signal extraction procedure (a) Physiological complex signal before and after algorithm processing. (b) Demodulated DCD signal before and after the proposed algorithm processing. (c) DRD and DHD signals extracted from the DCD signal. (d) Detail comparison between the DHD signal and the synchronously acquired BCG signal after filtering with a passband of 7–16 Hz.
表 1 毫米波雷达 IWR6843的主要参数
Table 1. Key parameters of millimeter wave radar IWR6843
参数 数值 载波频率 60 GHz 带宽 3.8 GHz 帧周期 4 ms 啁啾采样点数 100 距离仓宽度 3.94 cm 表 2 毫米波雷达 WRL6432的主要参数
Table 2. Key parameters of millimeter wave radar IWR6843
参数 数值 载波频率 60 GHz 带宽 6.63 GHz 帧周期 4 ms 啁啾采样点数 512 距离仓宽度 2.26 cm 表 3 仿真主要参数
Table 3. Key parameters of simulation
参数 数值 载波频率 60 GHz 带宽 3.8 GHz 帧周期 10 ms 啁啾采样点数 128 距离仓宽度 3.94 cm 表 4 距离FFT与本文算法处理信号的SNR对比
Table 4. 4 SNR comparison of the signal processed by range FFT and the proposed algorithm
Pos. Amp. SNR Before (dB) SNR After (dB) 0.00 1.00 24.69 42.05 0.25 1.00 23.50 35.60 0.50 1.00 24.29 36.87 0.00 0.50 22.91 43.44 0.25 0.50 27.73 42.16 0.00 0.25 26.54 45.66 0.50 0.50 29.84 43.04 0.25 0.25 32.28 44.69 表 5 DHD 信号的 C-C 间隔相对于 BCG 信号的 J-J 间隔的RMSE
Table 5. RMSE of the C-C interval of the DHD signal relative to the J-J interval of the BCG signal
人员 RMS (ms) 平均偏差(%) Subject 1 8.32 0.96 Subject 2 15.90 0.77 Subject 3 34.85 2.36 Subject 4 22.15 2.07 Subject 5 21.92 1.07 Subject 6 28.28 2.07 Subject 7 12.97 1.01 Subject 8 19.12 1.22 -
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