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基于FMCW雷达的非接触式医疗健康监测技术综述

方震 简璞 张浩 姚奕成 耿芳琳 刘畅宇 闫百驹 王鹏 杜利东 陈贤祥

周超, 刘泉华, 胡程. 间歇采样转发式干扰的时频域辨识与抑制[J]. 雷达学报, 2019, 8(1): 100–106. doi: 10.12000/JR18080
引用本文: 方震, 简璞, 张浩, 等. 基于FMCW雷达的非接触式医疗健康监测技术综述[J]. 雷达学报, 2022, 11(3): 499–516. doi: 10.12000/JR22019
ZHOU Chao, LIU Quanhua, and HU Cheng. Time-frequency analysis techniques for recognition and suppression of interrupted sampling repeater jamming[J]. Journal of Radars, 2019, 8(1): 100–106. doi: 10.12000/JR18080
Citation: FANG Zhen, JIAN Pu, ZHANG Hao, et al. Review of noncontact medical and health monitoring technologies based on FMCW radar[J]. Journal of Radars, 2022, 11(3): 499–516. doi: 10.12000/JR22019

基于FMCW雷达的非接触式医疗健康监测技术综述

DOI: 10.12000/JR22019
基金项目: 国家重点研发计划(2020YFC1512304, 2020YFC2003703),中国医学科学院医学与健康科技创新工程项目(2019-I2M-5-019)
详细信息
    作者简介:

    方 震(1976–),男,安徽巢湖人,中国科学院空天信息创新研究院研究员,博士生导师。研究方向为新型医疗电子检测与医学人工智能

    简 璞(1997–),男,安徽合肥人,中国科学院空天信息创新研究院在读硕士研究生。主要研究方向为智能医疗健康监测技术

    张 浩(1997–),男,山东济南人,中国科学院空天信息创新研究院在读博士研究生。主要研究方向为智能医疗健康监测技术和医疗物联网

    通讯作者:

    方震 zfang@mail.ie.ac.cn

  • 责任主编:吴一戎 Corresponding Editor: WU Yirong
  • 中图分类号: TN95; TP391

Review of Noncontact Medical and Health Monitoring Technologies Based on FMCW Radar

Funds: The National Key Research and Development Project (2020YFC1512304, 2020YFC2003703), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-019)
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  • 摘要: 非接触式的医疗健康监测系统解决了用户依从性问题,避免了佩戴电极、传感设备进行监测带来的不舒适感,更有助于将健康监测融入日常生活。非接触式监测手段具有持续地监测用户健康状况的潜力,能够在突发急性医疗事件出现时及时示警,且能够满足新生儿、烧伤患者、传染病患者等特殊人群的监测需求。调频连续波(FMCW)雷达能够同时捕获雷达视场内目标的距离、速度信息,可用于非接触式地监测用户的心率、呼吸率等生理体征及跌倒等行为动作,且从技术上易于单片集成,成本可控,因此在医疗健康监测领域有着重要的应用价值。该文首先阐述了将FMCW雷达应用于非接触式医疗健康监测技术的理论基础,然后系统性地归纳了该领域中的典型前沿应用,最后总结了基于FMCW雷达的医疗健康应用这一领域的研究现状及局限性,并对其应用前景与潜在的研究方向进行了展望。

     

  • 极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)具有全天时和几乎全天候的工作能力,通过收发极化状态正交的电磁波以获取目标的全极化散射信息[1]。地物分类是农作物生长监控、农村与城市用地普查、环境监测等应用领域的共性基础问题,也是极化SAR图像理解与解译的重要应用方向。高精度的地物分类结果能够为上述应用领域提供可靠的信息支撑。

    通常,提高极化SAR地物分类精度主要有两种途径[2]。第1种途径专注于极化特征的挖掘与优选,通过精细化的极化散射机理建模与解译,从全极化信息中提取出对不同地物类别具有更强区分度的特征。常用的极化散射机理解译方法有基于特征值分解的方法和基于模型分解的方法。基于这些极化目标分解方法所得到的极化特征参数经常被用于极化SAR地物分类,例如Cloude-Pottier分解所得的极化熵/极化平均角/极化反熵(H/ α/A)参数[3],Freeman-Durden分解[4]、Yamaguchi分解[5]和近年来提出的精细化极化目标分解[6]所得的各散射机理的散射能量参数(如奇次散射、偶次散射、体散射、螺旋散射等)[7]。第2种途径则从分类器入手,使用性能更好的分类器,以对现有的极化特征进行充分利用。常用的分类器包括C均值分类器、Wishart分类器、支持向量机(Support Vector Machine, SVM)分类器、随机森林分类器、神经网络分类器以及近来年在诸多领域取得成功应用的以卷积神经网络为代表的深度学习分类方法等[811]。当然,对特征和分类器同时进行优化和优选也是提高极化SAR地物分类精度的有效途径。

    在传统基于特征的极化SAR地物分类中,具有旋转不变特性的极化特征参数得到了广泛应用。例如,基于H/ α/A和总散射能量SPAN的极化SAR地物分类就是一种常用的分类方法。然而,目标的极化响应与目标和SAR的相对几何关系密切相关。同一目标在不同方位取向下,其后向散射可以是显著不同的。同时,不同目标在某些特定方位取向下,其后向散射又是十分相似的。例如,具有不同方位取向的建筑物与森林等植被就是极化SAR图像解译的难点。这是诸多传统极化目标分解方法存在散射机理解译模糊的重要原因之一,同时也限制了基于旋转不变极化特征参数的传统分类方法所得精度的进一步提升。为避免这种解译模糊,一种思路是构建更精细化的目标散射模型和精细化的极化目标分解方法。而另一种思路则是挖掘利用目标方位取向与其后向散射机理之间的隐含关系。文献[12]提出的统一的极化矩阵旋转理论就是一种代表性的方法。该方法提出了在绕雷达视线的旋转域中理解目标散射特性的新思路,并导出了一系列旋转域极化特征。部分旋转域极化特征参数已经在农作物辨识[13]、目标对比增强[12]、人造目标提取[14]等领域获得了成功应用。

    由于这些旋转域极化特征包含有目标在旋转域中隐含的极化散射信息,且与其方位取向具有一定关系。若将它们与传统的旋转不变极化特征参数于H/ α/A/SPAN联合作为地物分类特征集,则从极化特征挖掘的角度来看,两类不同的极化特征对于不同地物类别的区分能力势必会形成一定程度的互补,进而使分类精度得到进一步提升。基于这一思路,本文提出了一种结合旋转域极化特征与旋转不变特征H/ α/A/SPAN的极化SAR地物分类方法。具体即基于不同地物类别样本集类间距最大的特征优选准则,以部分优选的旋转域极化特征参数与H/ α/A/SPAN联合作为地物分类所用特征,并选用性能较为稳定的SVM[15]作为分类器进行分类处理。由于该分类方法额外使用了目标在方位取向方面的隐含信息,故相较于仅使用旋转不变特征H/ α/A/SPAN作为输入的SVM分类器[10],其能够达到更优的分类性能表现。

    本文第2节简要介绍了统一的极化矩阵旋转理论及其所导出的旋转域极化特征参数;第3节提出结合旋转域极化特征的极化SAR地物分类方法;第4节基于AIRSAR和多时相UAVSAR实测数据开展了地物分类对比实验及分析;第5节总结本文方法并对后续研究工作进行展望。

    极化SAR获得的目标全极化信息可以通过极化相干矩阵T表示。满足互易性原理时,极化相干矩阵T可以表示为:

    T=kPkHP=[T11T12T13T21T22T23T31T32T33] (1)

    其中, kP=12[SHH+SVVSHHSVV2SHV]T为Pauli散射矢量。 SHV为以垂直极化天线发射并以水平极化天线接收条件下的散射系数, kP中其它元素可类似定义。  表示集合平均。 Tij则表示极化相干矩阵 T中第i行第j列所对应的元素。

    将极化相干矩阵 T绕雷达视线进行旋转处理,则可得到旋转域中极化相干矩阵的表达式为:

    T(θ)=kP(θ)kHP(θ)=R3(θ)TRH3(θ) (2)

    其中,旋转矩阵为:

    R3(θ)=[1000cos2θsin2θ0sin2θcos2θ] (3)

    在旋转域中极化相干矩阵 T(θ)的每个元素经过相应的数学变换即可被统一地由一个正弦函数进行表征[12]

    f(θ)=Asin[ω(θ+θ0)]+B (4)

    其中,A为振荡幅度,B为振荡中心, ω为角频率, θ0为初始角度。文献[12]将这4类极化特征参数 {A,B,ω,θ0}称为振荡参数集,其完整表征极化相干矩阵的各元素在旋转域中的特性。这样就可以导出一系列旋转域极化特征参数,如表1所示。其中, Angle{a}表示复数a的相位,相应取值范围为 [π,π]

    表  1  旋转域极化特征参数[12]
    Table  1.  Polarimetric feature parameters derived from rotation domain[12]
    散射矩阵元素项 A= B ω θ0=1ωAngle{}
    Re[T12(θ)] Re2[T12]+Re2[T13] 0 2 Re[T13]+jRe[T12]
    Re[T13(θ)] Re2[T12]+Re2[T13] 0 2 Re[T12]+jRe[T13]
    Im[T12(θ)] Im2[T12]+Im2[T13] 0 2 Im[T13]+jIm[T12]
    Im[T13(θ)] Im2[T12]+Im2[T13] 0 2 Im[T12]+jIm[T13]
    Re[T23(θ)] 14(T33T22)2+Re2[T23] 0 4 12(T33T22)+jRe[T23]
    T22(θ) 14(T33T22)2+Re2[T23] 12(T22+T33) 4 Re[T23]+j12(T22T33)
    T33(θ) 14(T33T22)2+Re2[T23] 12(T22+T33) 4 Re[T23]+j12(T33T22)
    |T12(θ)|2 Re2[T12T13]+14(|T13|2|T12|2)2 12(|T12|2+|T13|2) 4 Re[T12T13]+j12(|T12|2|T13|2)
    |T13(θ)|2 Re2[T12T13]+14(|T13|2|T12|2)2 12(|T12|2+|T13|2) 4 Re[T12T13]+j12(|T13|2|T12|2)
    |T23(θ)|2 14{14(T33T22)2+Re2[T23]}2 12{14(T33T22)2+Re2[T23]}+Im2[T23] 8 12(T33T22)Re[T23]+j12[Re2[T23]14(T33T22)2]
    下载: 导出CSV 
    | 显示表格

    基于上述振荡参数集,文献[12]还导出了一系列的极化角参数集,如极化零角参数、极化最大化角参数以及极化最小化角参数等。其中,极化零角参数的定义为在绕雷达视线的旋转域中使极化相干矩阵某元素取值为零的旋转角,即:

    f(θ)=Asin[ω(θnull+θ0)]+B=0θnull=θ0 (5)

    其中, θnull即极化零角参数。由于表1中相互独立的5个初始角度 θ0分别为 θ0_Re[T12(θ)], θ0_Im[T12(θ)], θ0_Re[T23(θ)], θ0_|T12(θ)|2θ0_|T23(θ)|2,故相应的极化零角参数有 θnull_Re[T12(θ)], θnull_Im[T12(θ)], θnull_Re[T23(θ)], θnull_|T12(θ)|2θnull_|T23(θ)|2。由文献[12]可知,各初始角度与其相应极化零角参数所包含的极化信息是相互等价的,且极化零角参数具有相对明确的物理意义,故在本文的后续部分均以极化零角参数代替相应的初始角度。

    文献[12]使用极化零角参数 θnull_Re[T12(θ)]θnull_Im[T12(θ)]的组合能够成功辨识7类不同农作物,初步证实了极化零角参数集对于不同地物类别具有较好的区分能力。在此基础上,本文挖掘利用旋转域极化特征所蕴含目标在旋转域中的隐含信息,并将其应用于极化SAR地物分类。

    在此之前,需要基于地物分类的应用背景对众多的旋转域极化特征进行优选处理。在文献[12]所导出的一系列旋转域极化特征之中,以不同地物类别样本集相互之间的“类间距最大化”为准则,进行相应的旋转域极化特征优选。具体步骤为:首先对各旋转域极化特征参数进行归一化处理;然后将不同的地物类别两两组合形成若干的地物类别对;接着针对各地物类别对,以其中两地物类别之间的类间距为标准,优选出使其取值达到最大的旋转域极化特征,则每个地物类别对均对应于一个优选的旋转域极化特征;最后,将各地物类别对的优选结果进行“取并集”处理,进而得到最终的优选结果。

    文献[12]所导出相互独立的旋转域极化特征共有12个,分别为 θnull_Re[T12(θ)], θnull_Im[T12(θ)], θnull_Re[T23(θ)], θnull_|T12(θ)|2, θnull_|T23(θ)|2, A_Re[T12(θ)], A_Im[T12(θ)], A_T12(θ), A_ T23(θ), B_T12(θ), B_T33(θ), B_T23(θ)。针对之后实验部分所使用的AIRSAR数据(15类地物,两两组合形成105个地物类别对;其它说明见4.1节)以及多时相UAVSAR数据(7类地物,两两组合形成21个地物类别对;4个数据获取日期;其它说明见4.2节),上述特征优选流程所得结果如表2所示。

    表  2  针对不同极化SAR实测数据的特征优选结果
    Table  2.  Selected features for different PolSAR data
    实测数据 优选所得旋转域极化特征(相应地物类别对的个数)
    AIRSAR θnull_Re[T12(θ)](18), θnull_Im[T12(θ)](15), θnull_Re[T23(θ)](71), B_T33(θ)(1)
    UAVSAR 6月17日 θnull_Re[T12(θ)](5), θnull_Im[T12(θ)](12), θnull_Re[T23(θ)](4)
    6月22日 θnull_Re[T12(θ)](5), θnull_Im[T12(θ)](14), θnull_Re[T23(θ)](2)
    7月03日 θnull_Im[T12(θ)](3), θnull_Re[T23(θ)](18)
    7月17日 θnull_Re[T12(θ)](7), θnull_Im[T12(θ)](5), θnull_Re[T23(θ)](9)
    下载: 导出CSV 
    | 显示表格

    综合考虑表2中的优选结果,并在追求较高地物分类精度的同时,将两组实测数据优选得到的旋转域极化特征进行统一,故本文优选部分的最终结果为3个极化零角参数,即 θnull_Re[T12(θ)], θnull_Im[T12(θ)]θnull_Re[T23(θ)]

    为了将目标在旋转域中的隐含信息充分利用在极化SAR地物分类中,同时又发挥传统的旋转不变极化特征参数H/A/ α/SPAN在极化散射机理解译方面的优点,本文提出了一种结合旋转域极化特征的极化SAR地物分类方法,其流程图如图1所示,相应的具体操作如下:

    图  1  本文方法具体流程图
    Figure  1.  Flowchart of proposed method

    (1) 在进行Cloude-Pottier分解之前,需要对极化SAR数据进行相干斑滤波处理。本文采用新近提出的一种基于矩阵相似性检验的SimiTest自适应相干斑滤波方法[16]对极化SAR数据进行滤波预处理。

    (2) 基于滤波后的极化相干矩阵,计算总散射能量SPAN。

    (3) 同样地,基于滤波后的极化相干矩阵,进行Cloude-Pottier分解,得到极化特征量H/ α/A

    (4) 同时,将滤波后的极化相干矩阵绕雷达视线旋转,计算上述优选部分所得的3个极化零角参数。

    (5) 对上述7个极化特征参数分别进行归一化处理,以作为地物分类特征集输入至SVM分类器。

    (6) 通过SVM相应的训练与测试过程,实现对不同地物类别的分类处理。

    为了验证新极化特征(即3个旋转域极化零角参数)的引入对于传统地物分类方法性能的提升作用,在对极化相干矩阵中全部极化信息进行利用的前提之下,将本文方法与仅使用旋转不变特征H/A/ α/SPAN作为SVM分类器输入的传统方法进行对比。首先使用AIRSAR数据15类地物的分类验证本文方法的分类性能,再使用多时相UAVSAR数据7类地物的分类进一步验证本文方法对多时相数据的稳健性。在对此两组数据分别进行SimiTest相干斑滤波[16]时,所用滑窗大小均为15×15。对SVM分类器,各类地物样本的一半用于训练,另一半用于测试。

    本文首先使用NASA/JPL AIRSAR系统在荷兰Flevoland地区所获取的L波段全极化SAR数据进行地物分类实验。该数据方位向分辨率为12.1 m,距离向分辨率为6.6 m,所用区域大小为736×1010。SimiTest相干斑滤波后的Pauli RGB图如图2(a)所示。该区域的真值图如图2(b)所示,其中主要包含茎豆、豌豆、森林、苜蓿、小麦1、甜菜、土豆、裸地、草地、油菜籽、大麦、小麦2、小麦3、水域以及建筑物等15类地物。

    图  2  AIRSAR数据
    Figure  2.  AIRSAR data

    使用传统方法和本文方法分别对滤波后的数据进行分类处理,所得结果如图3所示。

    图  3  AIRSAR数据的分类结果
    Figure  3.  Classification results of AIRSAR data

    两种方法对AIRSAR数据15类地物分类处理所得精度如表3所示。通过比较可知,本文方法得到的总体分类精度为92.3%,优于传统方法91.1%的分类精度。且本文方法对草地77.3%的分类精度相较于传统方法的59.3%提升了18个百分点。另外,由于SVM分类器所用分类策略以总体分类精度的最大化为目标,无法保证单一地物类别的分类精度均达到最优。例如,本文方法在苜蓿、小麦1、裸地、大麦以及建筑物等5种地物类别区域所得分类精度均不及传统方法。针对其中分类精度差距最大(约8.3%)的裸地,由于其相应区域的主要散射机制为“面散射”,不同方位取向对其后向散射的影响较小,使用传统的旋转不变极化特征已经能较好地对其进行区分与辨识,本文方法额外引入的3个旋转域极化零角参数可能造成了分类信息的冗余,进而导致所得分类精度的较大幅度下降。

    表  3  两种方法所得AIRSAR数据15类地物及总体的分类精度(%)
    Table  3.  Classification accuracy of different terrains in AIRSAR data using two methods (%)
    地物 传统方法 本文方法
    茎豆 97.2 98.0
    豌豆 93.7 96.9
    森林 92.6 93.7
    苜蓿 96.8 96.6
    小麦1 88.7 85.9
    甜菜 93.8 93.8
    土豆 92.6 93.3
    裸地 95.5 87.2
    草地 59.3 77.3
    油菜籽 83.9 88.0
    大麦 92.6 91.5
    小麦2 89.2 89.4
    小麦3 94.3 95.9
    水域 98.0 98.5
    建筑物 84.9 83.2
    总体精度 91.1 92.3
    下载: 导出CSV 
    | 显示表格

    本文使用NASA/JPL UAVSAR系统在加拿大Manitoba地区所获取的多时相L波段全极化SAR数据进行地物分类实验。该数据方位向分辨率为7 m,距离向分辨率为5 m,所用区域大小为1325×1011。多时相极化SAR数据分别获取于6月17日、6月22日、7月3日以及7月17日。SimiTest相干斑滤波处理之后多时相极化SAR数据对应的Pauli RGB图如图4所示。该区域的主要地物类型是以谷物和油种产品为代表的混合型牧场农作物。相应的真值图如图5所示,其中主要包含阔叶林、草料、大豆、玉米、小麦、油菜籽以及燕麦等7类地物。

    图  4  多时相UAVSAR数据滤波后Pauli RGB图
    Figure  4.  Filtered Pauli RGB images of multi-temporal UAVSAR data
    图  5  所用区域的真值图
    Figure  5.  Gound truth of the multi-temporal data

    使用传统方法和本文方法分别对滤波后的多时相极化SAR数据进行相互独立的分类处理,所得结果分别如图6图7所示。

    图  6  传统方法对多时相UAVSAR数据分类结果
    Figure  6.  Classification results of multi-temporal UAVSAR data using conventional method
    图  7  本文方法对多时相UAVSAR数据分类结果
    Figure  7.  Classification results of multi-temporal UAVSAR data using proposed method

    图6(c)图7(c)所示,基于7月3日获取的数据,传统方法将红色圆框内小麦与燕麦的绝大部分错分为了大豆,而本文方法在该区域的分类性能相较于前者有显著提升。又如图6(d)图7(d)所示,基于7月17日获取的数据,传统方法将白色圆框内小麦的绝大部分错分为了大豆,而本文方法在该区域的分类精度相较于前者也有较大提升。

    两种方法对多时相UAVSAR数据7类地物分类处理所得精度如表4所示。通过比较可知,对不同日期获取的数据,本文方法所得各类地物及总体的分类精度均优于或相当于传统方法。其中,对6月17日、6月22日、7月3日以及7月17日4个不同日期所获取的数据,本文方法得到的总体分类精度分别为94.98%, 95.12%, 95.99%以及96.78%,而传统方法所得总体分类精度则波动于80.87%至90.75%之间,出现约10%的起伏。具体就小麦和燕麦而言,本文方法得到的分类精度均分别保持在94%和92%以上,而传统方法所得相应分类精度则分别出现了约30%和23%的波动起伏。另外,本文方法95.72%的平均总体分类精度相较于传统方法的87.80%提升了约8个百分点。故本文方法较好的分类性能对于同一系统的多时相数据更具稳健性。

    表  4  两种方法所得多时相UAVSAR数据7类地物及总体的分类精度 (%)
    Table  4.  The classification accuracy of different terrains in multi-temporal UAVSAR data using two methods (%)
    日期 方法 阔叶林 草料 大豆 玉米 小麦 油菜籽 燕麦 总体
    6月17日 传统 98.47 62.24 92.64 96.12 93.63 91.70 86.37 90.19
    本文 98.49 81.65 96.76 98.19 96.08 92.25 96.32 94.98
    6月22日 传统 98.05 61.38 94.14 97.30 97.89 93.82 77.29 90.75
    本文 97.96 72.60 96.86 98.18 97.07 96.84 95.13 95.12
    7月3日 传统 97.41 54.38 90.45 98.89 68.75 98.81 63.46 80.87
    本文 97.77 76.68 98.12 99.08 96.95 98.93 94.22 95.99
    7月17日 传统 96.86 64.51 97.38 99.78 84.76 92.19 82.98 89.39
    本文 97.27 93.15 99.31 99.58 94.73 99.71 92.16 96.78
    平均 传统 97.70 60.63 93.65 98.02 86.26 94.13 77.53 87.80
    本文 97.87 81.02 97.76 98.76 96.21 96.93 94.46 95.72
    下载: 导出CSV 
    | 显示表格

    另外,对于6月22日所获取数据中的阔叶林和小麦,以及7月17日所获取数据中的玉米,本文方法所得分类精度均略低于传统方法,且分类精度的差距均在1%以内。

    在上述两组相互独立的对比实验所得结果中,本文方法所得分类精度均优于传统方法。故本文方法所表现出的较好分类性能对于不同系统的数据也具有较强稳健性。

    目标方位取向对其后向散射响应的直接影响极易引起散射机理的解译模糊,进而限制仅使用旋转不变特征参数作为分类特征集的极化SAR地物分类所得精度。针对这一问题,本文将刻画目标旋转域隐含信息的旋转域极化特征用于极化SAR地物分类,并提出了一种结合旋转域极化特征和旋转不变特征H/A/ α/SPAN的极化SAR地物分类方法,该方法将旋转域极化零角参数和H/A/ α/SPAN联合作为分类特征集输入至SVM分类器。

    将本文方法与仅使用旋转不变特征H/A/ α/SPAN作为SVM分类器输入的传统方法进行比较:对AIRSAR数据15类地物分类而言,本文方法总体分类精度达到92.3%,优于传统方法的91.1%。对多时相UAVSAR数据7类地物分类而言,本文方法平均总体分类精度达到95.72%,显著优于传统方法的87.80%,表明本文方法对同一系统的多时相数据更具稳健性。这两组对比实验也表明本文方法较好的分类性能对于不同系统的数据具有较强稳健性。

    通过对旋转域中目标极化散射信息的深入挖掘,能够为极化SAR图像的解译与应用提供一条新的可行途径。下一步将考虑旋转域极化特征与具有深度学习能力的卷积神经网络等分类器相结合,以实现更高的分类精度。另外,对极化特征参数更优的选择准则及相互融合也是我们未来将要深入研究讨论的内容。

  • 图  1  基于FMCW雷达的接收信号提取微多普勒信号与计算Range-Doppler图的信号处理流程。其中多个Chirp的IF信号经过FFT变换后得到的二维矩阵称为Range Profile

    Figure  1.  The signal processing flow of extracting micro-Doppler signal and calculating Range-Doppler map based on the received signal of FMCW radar. The two-dimensional matrix of multiple Chirp IF signals after FFT transform is called Range Profile

    图  2  SFCW雷达信号时频图

    Figure  2.  Time-frequency graph of SFCW radar

    图  3  FMCW雷达天线阵列计算Range-Angle图的原理示意图

    Figure  3.  The schematic diagram of Range-Angle diagram based on FMCW radar antenna array

    表  1  基于FMCW雷达的心率、呼吸率监测研究现状总结

    Table  1.   Summary of heart rate and respiratory rate monitoring based on FMCW radar

    作者信号获取方法生理参数估计方法实验设置监测指标
    Adib等人[21]微多普勒信号提取频谱分析被试者保持静止,与雷达相距1 mRR准确率中位数为99.3%,HR准确率中位数为98.5%
    Mercuri等人[56]微多普勒信号提取频谱分析2名被试者,静止,与雷达距离分别为2.6 m, 5.4 m98.5%的RR估计误差小于3 次/
    min, 95.5%的HR估计误差小于3 次/min
    Wang等人[22]Beamforming,微多普勒信号提取频谱分析3名被试者,2名被试者与雷达距离1 m,AOA相差60°,1名被试者与雷达距离为1.5 m,保持静止RR, HR平均准确率大于92.8%
    Chen等人[58]相控阵技术,微多普勒信号提取频谱分析2名被试者,与雷达距离相同,约2 m,被试者间距离1 m,静止97.8%的RR估计误差小于1.5 次/min, 93.6%的HR估计误差小于3 次/min
    Sun等人[59]EMD,微多普勒信号提取频谱分析被试者与雷达间距1.0~2.5 m,静止HR估计误差RMSE为2.03~5.83 次/min
    Wang等人[45]VMD,微多普勒信号提取峰值检测被试者与雷达间距0.5~2.0 m,AOA为0~60°,静止IBI RMSE为29.850~68.974 ms
    Toda等人[60]CNN,微多普勒信号提取QRS波群检测被试者静止,距离2.5 mIBI MAE为17.8 ms
    Ha等人[61]Beamforming,CNN,微多普勒信号提取Unet[62]被试者静止,面向雷达,距离25~50 cm心脏收缩期、舒张期等心脏活动检测准确率90%,召回率为69.8%
    Zheng等人[63]CFAR,多变量VMD,微多普勒信号提取频谱分析,峰值检测被试者驾驶汽车,在不同路况下行驶RR 误差中位数为 0.06 次/min, HR MAE误差中位数为 0.6 次/
    min, IBI误差中位数约50 ms
    Chen等人[64]深度对比学习算法,微多普勒信号提取频谱分析,峰值检测被试者存在步行,坐下/站起等大幅度肢体运动RR HR的MAPE为2% ,3%;不同肢体运动下IBI误差中位数为20~40 ms
    下载: 导出CSV

    表  2  基于FMCW雷达的跌倒检测研究现状总结

    Table  2.   Summary of research status of falling detection based on FMCW radar

    作者雷达特征信息算法概述是否在新用户/
    新环境下测试
    是否需要
    采集跌倒样本
    非跌倒/跌倒
    样本比例
    检测指标
    Jokanovic等人[125]Range Profile,Range-Doppler图Autoencoder+
    Logistic回归
    43:17Acc: 96%
    Tian等人[128]Range-Angle图级联CNN分类器450000:293F1: 0.929
    元志安等人[126]Range-Doppler图CNN+LSTM1:1Acc: 96.67%
    Wang等人[127]IF信号LKCNN1:1Acc: 95.24%
    Jin等人[46]点云VAE+RNN4:1Acc: 98%
    Li等人[40]Range-Doppler图LSTM5:1Acc: 96%
    下载: 导出CSV
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  • 收稿日期:  2022-01-19
  • 修回日期:  2022-03-04
  • 网络出版日期:  2022-03-31
  • 刊出日期:  2022-06-28

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