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基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究

潘俊 叶盛波 史城 倪志康 郑之杰 方广有

潘俊, 叶盛波, 史城, 倪志康, 郑之杰, 方广有. 基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究[J]. 电子与信息学报, 2022, 44(4): 1241-1248. doi: 10.11999/JEIT211042
引用本文: 潘俊, 叶盛波, 史城, 倪志康, 郑之杰, 方广有. 基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究[J]. 电子与信息学报, 2022, 44(4): 1241-1248. doi: 10.11999/JEIT211042
PAN Jun, YE Shengbo, SHI Cheng, NI Zhikang, ZHENG Zhijie, FANG Guangyou. Study on Respiration Signal Detection Algorithm of Ultra-WideBand Through-wall Radar Based on A Priori Signal-to-Noise Ratio Estimation[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1241-1248. doi: 10.11999/JEIT211042
Citation: PAN Jun, YE Shengbo, SHI Cheng, NI Zhikang, ZHENG Zhijie, FANG Guangyou. Study on Respiration Signal Detection Algorithm of Ultra-WideBand Through-wall Radar Based on A Priori Signal-to-Noise Ratio Estimation[J]. Journal of Electronics & Information Technology, 2022, 44(4): 1241-1248. doi: 10.11999/JEIT211042

基于先验信噪比估计的超宽带穿墙雷达呼吸信号检测算法研究

doi: 10.11999/JEIT211042
基金项目: 国家自然科学基金(61827803),科技部重点研发计划(2018YFC0810200)
详细信息
    作者简介:

    潘俊:男,1994年生,博士生,研究方向为穿墙雷达目标检测与成像方法

    叶盛波:男,1983年生,研究员,硕士生导师,研究方向为超宽带雷达系统设计及微波信号处理与成像算法

    史城:男,1994年生,博士生,研究方向为超宽带雷达系统和信号处理技术

    倪志康:男,1995年生,博士生,研究方向为探地雷达信号处理和机器学习

    郑之杰:男,1997年生,博士生,研究方向为穿墙人体姿态估计和活动识别、深度学习和跨模态学习

    方广有:男,1963年生,研究员,博士生导师,研究方向为超宽带雷达成像理论与方法、探地雷达技术、地下资源电磁勘探技术、月球/火星探测雷达技术、超宽带天线理论与技术、左手材料、THz成像技术等

    通讯作者:

    方广有 gyfang@mail.ie.ac.cn

  • 中图分类号: TN957.52

Study on Respiration Signal Detection Algorithm of Ultra-WideBand Through-wall Radar Based on A Priori Signal-to-Noise Ratio Estimation

Funds: The National Natural Science Foundation of China (61827803), The Key R&D Program of the Ministry of Science and Technology (2018YFC0810200)
  • 摘要: 废墟下呼吸信号的检测对地震救援具有重要意义。在实际中,障碍物(如墙体)后的人体呼吸信号会被环境中的噪声所掩盖。如何提升穿墙呼吸信号的信噪比(SNR)仍是一项具有挑战性的工作。该文提出一种基于先验信噪比估计的检测算法,用于增强穿墙弱呼吸信号的输出SNR。该算法在谱减法中典型的决策导向(DD)算法基础上加入了自适应权重因子,通过降低先验信噪比的估计误差来进一步消除残余随机噪声。通过仿真和实验验证了所提出算法的性能。与传统的快速傅里叶变换(FFT)、奇异值分解(SVD)和DD检测算法相比,所提出的呼吸检测算法的输出SNR有所提高。
  • 图  1  呼吸检测算法的信号处理流程图

    图  2  仿真模型

    图  3  各算法的频率-距离结果

    图  4  不同输入SNR下4种算法的输出SNR

    图  5  穿墙人体呼吸检测实验场景

    图  6  各算法的实验结果

    图  7  频率-距离图像的投影结果

    表  1  不同墙体厚度下的输出SNR (dB)

    墙厚(m)
    0.120.240.37
    FFT算法12.1311.1010.06
    SVD算法18.9717.2814.37
    DD算法26.3423.3018.41
    所提算法32.8330.8027.67
    下载: 导出CSV

    表  2  UWB雷达参数

    参数参数值
    信号模式高斯脉冲
    中心频率500 MHz
    带宽500 MHz
    瞬时功率26 dBm
    快时间等效采样率16 GHz
    慢时间采样率13 Hz
    下载: 导出CSV

    表  3  两个静止目标的输出SNR (dB)

    目标
    目标1目标2
    FFT算法9.377.49
    SVD算法10.308.73
    DD算法14.8912.39
    所提算法21.6818.73
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-09-27
  • 修回日期:  2021-12-27
  • 录用日期:  2021-12-28
  • 网络出版日期:  2022-01-23
  • 刊出日期:  2022-04-18

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