不同场景下UWB雷达探测呼吸心跳信号研究现状

郑学召 丁文 黄渊 蔡国斌 马扬 刘盛铠 周博

郑学召, 丁文, 黄渊, 等. 不同场景下UWB雷达探测呼吸心跳信号研究现状[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24154
引用本文: 郑学召, 丁文, 黄渊, 等. 不同场景下UWB雷达探测呼吸心跳信号研究现状[J]. 雷达学报(中英文), 待出版. doi: 10.12000/JR24154
ZHENG Xuezhao, DING Wen, HUANG Yuan, et al. The current research status of UWB radar detection of respiration and heartbeat signals in different scenarios[J]. Journal of Radars, in press. doi: 10.12000/JR24154
Citation: ZHENG Xuezhao, DING Wen, HUANG Yuan, et al. The current research status of UWB radar detection of respiration and heartbeat signals in different scenarios[J]. Journal of Radars, in press. doi: 10.12000/JR24154

不同场景下UWB雷达探测呼吸心跳信号研究现状

DOI: 10.12000/JR24154
基金项目: 国家自然科学基金(52174197),国家重点研发计划(2023YFC3010905),陕西省科协青年人才托举计划(20240205)
详细信息
    作者简介:

    郑学召,博士,教授,博士生导师,主要研究方向为矿山应急技术与管理、UWB雷达生命信息探测技术

    丁 文,硕士生,主要研究方向为矿山应急技术与管理、UWB雷达回波信号处理、有效特征提取与识别

    黄 渊,博士生,主要研究方向为矿山应急技术与管理、UWB雷达回波特征

    蔡国斌,博士生,主要研究方向为矿山应急救援、UWB雷达弱回波信号增强与提取

    马 扬,硕士生,主要研究方向为矿山应急技术与管理、UWB雷达回波信号处理、人员定位与量化识别

    刘盛铠,硕士生,主要研究方向为矿山应急技术与管理、激光雷达信号处理

    周 博,硕士生,主要研究方向为矿山应急救援、UWB雷达回波特征提取

    通讯作者:

    丁文 2695900258@qq.com

    黄渊 hy_xust@163.com

  • 责任主编:金添 Corresponding Editor: JIN Tian
  • 中图分类号: TD77

The Current Research Status of UWB Radar Detection of Respiration and Heartbeat Signals in Different Scenarios

Funds: The National Natural Science Foundation of China (52174197), The National Key Research and Development Program of China(2023YFC3010905), Shaanxi Provincial Association for Science and Technology Young Talent Support Program (20240205)
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  • 摘要: UWB雷达由于具有结构简单、发射功率低、穿透能力强、分辨能力高、传输速度快等诸多优势,逐渐成为多探测场景广泛应用的生命信息探测技术及装备。要完成生命信息的有效探测,关键是利用雷达回波信息处理技术从UWB雷达回波中提取被测人员的呼吸心跳信号,这对不同场景实现生命信息的判定、位置信息的获取、疾病的监测和预防以及保障人员安全具有至关重要的意义。为此,该文介绍了UWB雷达及分类、电磁散射机理和探测原理;分析了呼吸心跳信号的雷达回波模型构建现状;从时域、频域、时频域分析方法等角度梳理了现有呼吸心跳信号的提取方法;并从矿山救援、地震救援、医疗健康、穿墙探测等场景归纳了呼吸心跳信号提取的研究进展。总结了当前研究中存在的主要问题,展望了未来研究工作应重点关注的领域。

     

  • 图  1  UWB雷达优点和应用领域

    Figure  1.  UWB radar advantages and application areas

    图  2  脉冲波UWB雷达回波时延

    Figure  2.  Pulsed-wave UWB radar echo time delay

    图  3  UWB雷达探测原理示意图

    Figure  3.  Schematic of UWB radar detection principle

    图  4  呼吸心跳的多普勒探测原理

    Figure  4.  Principle of Doppler detection of respiratory heartbeat

    图  5  雷达探测的实际呼吸心跳波形

    Figure  5.  Actual respiratory heartbeat waveform detected by radar

    图  6  正弦与尖脉冲呼吸心跳拟合波形对比[25]

    Figure  6.  Comparison of fitted waveforms for sinusoidal and apical pulse respiratory heartbeats[25]

    图  7  雷达回波模型构建过程示意图

    Figure  7.  Schematic diagram of the radar echo model construction process

    图  8  雷达回波在时域、频域的关系示意

    Figure  8.  Schematic of the relationship between radar echo in time and frequency domains

    图  9  小波变换分解重构原理[42]

    Figure  9.  Principle of wavelet transform decomposition and reconstruction[42]

    图  10  VMD-WOA算法流程[58]

    Figure  10.  Flow of VMD-WOA algorithm[58]

    图  11  文献[63]重构信号的频谱

    Figure  11.  Spectrogram of reconstructed signal from Ref. [63]

    图  12  UWB雷达在矿山救援中的应用示意图

    Figure  12.  Schematic diagram of the application of UWB radar in mine rescue

    图  13  不同信噪比环境下各算法信号提取性能[74]

    Figure  13.  Signal extraction performance of each algorithm in different SNR environments [74]

    图  14  文献[86]实验结果及现有同类方法对比

    Figure  14.  Comparison of experimental results of Ref. [86] and existing similar methods

    图  15  自适应小波尺度选择方法的呼吸心跳分离效果[89]

    Figure  15.  Respiratory heartbeat separation effect of adaptive wavelet scale selection method [89]

    图  16  文献[90]所提方法与接触式传感器、带通滤波器、EMD算法和WT算法分离效果对比

    Figure  16.  Comparison of the separation effect of the proposed method in Ref. [90] with contact sensor, bandpass filter, EMD algorithm and WT algorithm

    图  17  文献[93]提取的呼吸波形

    Figure  17.  Respiratory waveforms extracted from Ref. [93]

    图  18  文献[97]雷达与心电图探测心跳信号LF/HF比值的比较

    Figure  18.  Comparison of LF/HF ratio of heartbeat signals detected by radar and ECG in Ref. [97]

    图  19  文献[33]所提方法与现有方法提取心跳信号准确度对比

    Figure  19.  Comparison of accuracy of extracting heartbeat signals between the proposed method in Ref. [33] and existing methods

    图  20  文献[21]所提方法与IIR, FFT和CEEMDAN的呼吸心跳信号分离效果对比

    Figure  20.  Comparison of the effectiveness of the proposed method in Ref. [21] with IIR, FFT and CEEMDAN for the separation of respiratory heartbeat signals

    图  21  UWB雷达监测驾驶员状况的应用

    Figure  21.  Application of UWB radar to monitor driver status

    图  22  文献[102]探测结果

    Figure  22.  Ref. [102] detection results

    表  1  UWB雷达分类对比

    Table  1.   Comparison of UWB radars classifications

    类别 连续波 脉冲波
    调频连续波 步进频率连续波 高斯脉冲波
    探测原理 多普勒效应、干涉相位 回波时延、干涉相位、多普勒效应
    表达方程 $S(t) = A{\text{cos}}({\text{2\pi }}{f_0}t + {\pi }K{t^2})$
    其中,A为振幅,$ {f_0} $为初始频率,K为调频斜率
    $S(t) = A{\text{cos}}({{2\pi }}({f_0}t + n\Delta f)t)$
    其中,A为振幅,$ {f_0} $为初始频率,$\Delta f$为频率步进间隔,n为当前步进的索引
    $S(t) = \sum\limits_n {Ap} (t - nT)$
    其中,A为振幅,$ p(t) $为脉冲形状函数,T为脉冲间隔
    波形图像
    优点 高距离分辨率、高速度分辨率 高距离分辨率、高图像分辨率 高时间、频率分辨率,脉冲持续时间极短(纳秒级),利于探测微弱信号
    缺点 存在非线性问题、不适用于远距离探测 制造难度大、信号处理复杂、不适用于远距离探测 距离分辨率受到脉冲宽度限制、需要高采样率、模数转换器要求高
    应用领域 医疗健康(跌倒检测)、汽车雷达、无人机避障雷达 医疗健康、交通监控 矿山救援与地震救援等需穿透障碍物的复杂灾害环境
    下载: 导出CSV

    表  2  干涉相位在连续波、脉冲波UWB雷达系统的优缺点对比

    Table  2.   Comparison of advantages and disadvantages of interferometric phase in continuous wave and pulsed wave UWB radar systems

    雷达系统优点缺点
    连续波UWB雷达实时动态监测能力强时域分辨率低、信号处理复杂
    脉冲波UWB雷达时域分辨率高、多径分析能力强脉冲信号的不连续性造成相位跳变,增加信号处理难度
    下载: 导出CSV

    表  3  呼吸心跳运动参数

    Table  3.   Respiratory heartbeat exercise parameters

    生命体征 频率(Hz) 胸腔振幅(mm) 胸腔振动面积(cm3)
    呼吸 0.1~0.5 4~12 50
    心跳 0.8~2.0 0.2~0.5 10
    下载: 导出CSV

    表  4  时域、频域和时频域分析方法对比

    Table  4.   Comparison of time-domain, frequency-domain and time-frequency-domain analysis methods

    方法 优点 缺点 适用场景 典型方法
    时域分析
    方法
    易实现、适应性强、计算量小
    且效率快、便于直接观察
    低信噪比环境处理能力不强、
    易丢失信息、抗干扰性差
    用于睡眠、临床、健康检测
    的呼吸心跳信号提取
    时域相关法、脉冲压缩法、
    时域差分法
    频域分析
    方法
    频谱精度高、计算量小,对幅度
    变化较大的信号处理能力强
    存在边缘效应与信号失真、
    不能反映信号的时变特性
    用于滤波去噪,在医疗健康
    场景中提取呼吸心跳信号
    傅里叶变换、快速傅里叶变换、
    频谱减法
    时频分析
    方法
    利于非线性、不平稳及瞬时信号
    的处理、多分辨率强、准确性高
    运算复杂、性能依赖参数
    选择、存在边缘效应
    用于灾害救援、医疗健康检测
    等场景的多种信号处理任务
    短时傅里叶变换、小波变换、希尔
    伯特-黄变换、模态分解系列算法
    下载: 导出CSV

    表  5  EMD算法重构信号的频谱峰值

    Table  5.   Spectral peaks of signal reconstructed by EMD algorithm

    重构信号 频谱峰值
    重构呼吸信号 0.1~0.5 Hz
    重构心跳信号 0.8~2.0 Hz
    噪声干扰 其他频率
    下载: 导出CSV

    表  6  部分模态分解算法的优缺点对比

    Table  6.   Comparison of advantages and disadvantages of some modal decomposition algorithms

    方法优点缺点
    EMD适应性较强、探测效果较好、速度快存在模态混叠和虚假分量、IMF物理意义模糊
    EEMD无模态混叠现象、IMF物理意义明确计算量大、工作效率低、模块响应时间长
    CEEMD计算量小、响应速度快、可消除重构信号的噪声依据经验选择参数、不适用于低信噪比环境
    VMD可有效避免模态混叠和端点效应,探测性能强计算时间长、参数选择效率低、实时性差
    下载: 导出CSV

    表  7  各神经网络方法的性能及优缺点对比

    Table  7.   Comparison of performance and advantages and disadvantages of each neural network method

    方法 性能 优点 缺点
    卷积神经
    网络
    可有效处理复杂信号且捕捉信号中的
    时空相关性
    高效的数据处理能力,精度高、
    鲁棒性强、适应性强
    需预定义不同大小的卷积核,数据库要求
    高,且存在退化现象、难以提高精度
    残差神经
    网络
    可训练非常深的网络且可提取微弱信号 深度加深而计算量不增加、无退化现象
    和梯度消失问题、训练效率高
    计算量大、数据需求量大、
    设计和调试难度大
    前馈人工神经
    网络
    可实现小数据集输入信号到输出结果的
    非线性映射
    结构简单、计算效率高 难以捕捉信号的时空相关性,对输入信号的
    设计和选择要求较高
    循环神经
    网络
    可提取序列化数据的动态信息并进行分类,
    可学习特征在时间上的周期关系
    非独立的点序列组信号处理能力及
    适应性强、记忆前序信息
    计算成本高、输入信息易损失,反向传播长
    时依赖下存在梯度爆炸和梯度消失
    长短期记忆
    网络
    可有效捕捉信号中时序依赖关系且记忆
    长期的依赖关系
    可有效处理长时间依赖性,序列数据
    适应性强,缓解梯度消失问题
    计算量大、参数多且调节难度大
    下载: 导出CSV

    表  8  各场景信号提取面临的问题与难点

    Table  8.   Problems and difficulties faced in signal extraction for each scene

    场景问题、难点
    矿山救援场景穿透能力与分辨率相互制约
    地震救援场景背景杂波和电磁干扰严重
    医疗健康场景实时性和准确性要求更高
    穿墙探测场景三维数据显示精度要求高
    汽车驾驶场景躯体、车辆抖动干扰严重
    下载: 导出CSV
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  • 收稿日期:  2024-08-07
  • 修回日期:  2024-09-17

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