Zhang Jingwen, Qi Qingjie, Cheng Huifeng, Sun Lifeng, Liu Siyun, Wang Yue, Jia Xinlei
Emergency Research Institute, Chinese Institute of Coal Science CICS, Beijing 100013, China.
Sensors (Basel). 2023 Jun 21;23(13):5779. doi: 10.3390/s23135779.
Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.
基于超宽带(UWB)雷达的生命探测技术是一种非接触式主动探测技术,可用于灾难救援中搜寻幸存者。现有的基于UWB雷达回波信号的多目标探测方法精度较低,且难以同时提取呼吸和心跳信息。因此,本文提出了一种基于超宽带雷达的多目标定位与生命体征检测新方法。提出了一种基于排列熵(PE)和K均值++聚类的目标识别与定位方法,以确定环境中目标的数量和位置。提出了一种基于集成经验模态分解(EEMD)和小波分析(WA)的生命体征提取自适应去噪方法,以重构人体目标的呼吸和心跳信号。提出了一种基于粒子群优化(PSO)和随机共振(SR)的心跳频率提取方法,以检测人体目标的心跳频率。实验结果表明,PE-K均值++方法能够成功识别和定位环境中的多个人体目标,其平均相对误差为1.83%。使用EEMD-WA方法能够有效滤除杂波信号,重构呼吸信号频率的平均相对误差为4.27%。PSO-SR方法检测心跳频率的平均相对误差为6.23%。本文提出的多目标定位与生命体征检测方法能够有效识别多目标场景中的所有人 体目标,并提供其准确的位置和生命体征信息。这为应急救援技术体系提供了理论依据,为灾后救援提供了技术支持。