基于距离抽头重构的生理雷达动态解调算法

刘畅宇; 张浩; 耿芳琳; 白忠瑞; 王鹏; 李振锋; 杜利东; 陈贤祥; 方震

doi:10.12000/JR24151

基于距离抽头重构的生理雷达动态解调算法

DOI: 10.12000/JR24151

刘畅宇^{1, 2},
张浩^{1, 2},
耿芳琳^{1, 2},
白忠瑞⁴,
王鹏^{1, 2, 3},
李振锋^{1, 2, 3},
杜利东^{1, 2, 3},
陈贤祥^{1, 2, 3},
方震^{1, 2, 3, ,}

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院大学电子电气与通信工程学院北京 100049
3.
中国医学科学院个性化呼吸慢病管理创新单元北京 100094
4.
上海交通大学电子信息与电气工程学院上海 200030

基金项目: 国家自然科学基金(62331025, U21A20447, 62071451)，国家重点研发计划项目(2021YFC3002204)，中国医学科学院医学科学创新基金(2019-I2M-5-019)

详细信息

作者简介:
刘畅宇，博士生，主要研究方向为智能医疗健康监测技术和医疗物联网

张　浩，博士生，主要研究方向为智能医疗健康监测技术和医疗物联网

方　震，研究员，博士生导师，主要研究方向为新型医疗电子检测与医学人工智能

通讯作者:
方震 zfang@mail.ie.ac.cn

责任主编：谢磊 Corresponding Editor: XIE Lei
中图分类号: TN95; TP391
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- 文章访问数: 162
- HTML全文浏览量: 55
- PDF下载量: 28
- 被引次数: 0
出版历程
- 收稿日期: 2024-07-23
- 修回日期: 2024-10-08
- 网络出版日期: 2024-12-10

Dynamic Demodulation Algorithm for Bio-radar Sensors Based on Range Tapper

LIU Changyu^{1, 2},
ZHANG Hao^{1, 2},
GENG Fanglin^{1, 2},
BAI Zhongrui⁴,
WANG Peng^{1, 2, 3},
LI Zhenfeng^{1, 2, 3},
DU Lidong^{1, 2, 3},
CHEN Xianxiang^{1, 2, 3},
FANG Zhen^{1, 2, 3
, ,}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences,Beijing 100049, China
3.
Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, Beijing 100094, China
4.
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University Shanghai 200030, China

Funds: The National Natural Science Foundation of China (62331025, U21A20447, 62071451), National Key Research and Development Project (2021YFC3002204), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-019)

More Information

Corresponding author: FANG Zhen, zfang@mail.ie.ac.cn

摘要

摘要: 在无感雷达体征监测中，与连续波雷达(CW)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快速傅里叶变换(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，信噪比(SNR)提升了17±5 dB。此外，实验通过获取8名受试者的多普勒心跳图(DHD)信号，定量分析了DHD信号与心冲击图 (BCG)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%)。
- 毫米波无线电 /
- 无线传感 /
- 心率监测 /
- 非接触式传感 /
- 调频连续波
Abstract: In non-inductive radar vital sign monitoring, frequency-modulated radars (such as Frequency Modulated Continuous Wave (FMCW) and Ultra WideBand (UWB)) are more effective than Continuous Wave (CW) 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 (FFT) 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 BallistoCardioGram (BCG) signal was 21.58±13.26 ms (3.40%±2.08%).
- Millimeter-wave radio /
- Wireless sensing /
- Heart rate monitoring /
- Non-contact sensing /
- Frequency modulated continuous wave

HTML全文

图 1 距离抽头重构与动态解调算法

Figure 1. Range tapper refactoring and dynamic demodulation algorithms

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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10.0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10.0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 10.0 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 10.0 and the displacement reaches the width of one range bin

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图 3 跨越距离仓的条件

Figure 3. The conditions of crossing range bin

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图 4 幅度调制信号经过不同解调算法提取的DCD

Figure 4. DCD extracted from amplitude modulated signals using different demodulation algorithms

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图 5 幅度调制生理复信号解调后的功率谱密度

Figure 5. Power spectral density of amplitude modulated physiological complex signal after arctan demodulation

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图 6 本文算法处理后DCD信号的功率谱密度

Figure 6. Power spectral density of DCD signal after processed by the proposed algorithm

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图 7 信号处理流程

Figure 7. Data processing chain

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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

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图 9 仿真结果

Figure 9. Simulation results

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图 10 跨距离仓实验相位解调结果

Figure 10. Cross range bin phase demodulation results

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图 11 跨距离仓实验结果

Figure 11. Cross range bin results

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图 12 DHD信号提取过程

Figure 12. DHD signal extraction procedure

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图 13 检测到的8名受试者的DHD信号(滤波器通带为8～20 Hz以及每段信号3～5 s的DHD信号细节)

Figure 13. Detected DHD signals for each eight subjects (details of the DHD signals between 3 and 5 s where the filter passband is 8～20 Hz)

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图 14 DHD信号中C-C间隔与BCG信号中J-J间隔的关系

Figure 14. Relationship between C-C interval in DHD signal and J-J interval in BCG signal

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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

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表 2 毫米波雷达WRL6432的主要参数

Table 2. Key parameters of millimeter-wave radar IWR6432

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

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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

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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	1.00	24.69	42.05
0.25	1.00	23.50	35.60
0.50	1.00	24.29	36.87
0	0.50	22.91	43.44
0.25	0.50	27.73	42.16
0	0.25	26.54	45.66
0.50	0.50	29.84	43.04
0.25	0.25	32.28	44.69

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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	RMSE (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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