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The Current Research Status of UWB Radar Detection of Respiration and Heartbeat Signals in Different Scenarios
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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。Abstract: Due to their many advantages, such as simple structure, low transmission power, strong penetration capability, high resolution, and high transmission speed, UWB (Ultra-Wide Band) radars have been widely used for detecting life information in various scenarios. To effectively detect life information, the key is to use radar echo information–processing technology to extract the breathing and heartbeat signals of the involved person from UWB radar echoes. This technology is crucial for determining life information in different scenarios, such as obtaining location information, monitoring and preventing diseases, and ensuring personnel safety. Therefore, this paper introduces a UWB radar and its classification, electromagnetic scattering mechanisms, and detection principles. It also analyzes the current state of radar echo model construction for breathing and heartbeat signals. The paper then reviews existing methods for extracting breathing and heartbeat signals, including time domain, frequency domain, and time–frequency domain analysis methods. Finally, it summarizes research progress in breathing and heartbeat signal extraction in various scenarios, such as mine rescue, earthquake rescue, medical health, and through-wall detection, as well as the main problems in current research and focus areas for future research.
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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 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
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 
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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
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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
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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
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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
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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
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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
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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
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