复杂场景下基于UWB雷达的呼吸特征检测算法  

Respiratory feature detection algorithm based on UWB radar in complex scene

作　　者：崔学荣[1] 杨雷[1] 李娟[1] 李世宝[1] Cui Xuerong;Yang Lei;Li Juan;Li Shibao(China University of Petroleum(East China),Qingdao 266000,China)

机构地区：[1]中国石油大学(华东),青岛266000

出　　处：《电子测量技术》2021年第4期70-74,共5页Electronic Measurement Technology

基　　金：国家自然科学基金(61902431,91938204,61972417);山东省重点研发计划(2019GGX101048);中央高校基本科研业务费专项资金(19CX05003A-9,19CX05003A-4,18CX02136A)资助。

摘　　要：超宽带(UWB)雷达作为一种新兴的生命体征探测方式,可对人体呼吸体征进行非接触式实时监测,在医学上具有重大意义。针对在复杂场景下雷达回波信号的距离门无法被准确自动提取的问题,提出了一种使用神经网络对雷达回波信号中快时域距离门的滤波方法,分析并设计了全连接神经网络的网络结构,提高人体呼吸体征实时检测的精度。用PulsON-440 UWB雷达模块采集数据,在实际场景中,完成了实验对比分析,结果表明,所提方法可有效去除非呼吸的距离门信号,并可进一步提高呼吸信号的质量和探测的鲁棒性。Ultra-wideband(UWB) radar, as a new method of vital sign detection, can monitor human respiratory signs in real-time and non-contact mode. It is of great medical significance. To solve the problem that the distance gate of radar echo signal cannot be extracted accurately and automatically in complex scenes. A fast time domain distance gate filtering method for radar echo signals using neural network is presented. The network structure of the fully connected neural network is analyzed and designed to improve the accuracy of real-time detection of human respiratory signs. Data is collected using PulsON-440 UWB radar module. In the actual scene, the comparative analysis of experiments is completed. The results show that this method can effectively remove the non-breathing distance gate signal. It can further improve the quality of respiratory signals and robustness of detection.

关 键 词：超宽带雷达 呼吸信号 神经网络 距离门 

分 类 号：TN957.51[电子电信—信号与信息处理]