心跳
窄带
计算机科学
雷达
脉冲多普勒雷达
雷达工程细节
算法
信号(编程语言)
多普勒效应
多普勒雷达
电子工程
带宽(计算)
雷达跟踪器
连续波雷达
实时计算
电信
雷达成像
工程类
物理
程序设计语言
计算机安全
天文
作者
De-Ming Chian,Chao-Kai Wen,Fu-Kang Wang,Kai‐Kit Wong
标识
DOI:10.1109/tbcas.2020.3029709
摘要
Noninvasive monitoring is an important Internet-of-Things application, which is made possible with the advances in radio-frequency based detection technologies. Existing techniques however rely on the use of antenna array and/or frequency modulated continuous wave radar to detect vital signs of multiple adjacent objects. Antenna size and limited bandwidth greatly limit the applicability. In this paper, we propose our system termed `DeepMining' which is a single-antenna, narrowband Doppler radar system that can simultaneously track the respiration and heartbeat rates of multiple persons with high accuracy. DeepMining uses a number of signal observations over a period of time as input and returns the trajectory of the respiration and heartbeat rates of each person. The extraction is based on frequency separation algorithms using successive signal cancellation. The proposed system is implemented using the self-injection locking radar architecture and tested in a series of experiments, showing accuracies of 90% and 85% for two and three objects, respectively, even for closely located persons.
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