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用于超宽带雷达传感器生命体征提取的SSA-VMD算法

SSA-VMD for UWB Radar Sensor Vital Sign Extraction.

作者信息

Yu Huimin, Huang Wenjun, Du Baoqiang

机构信息

College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China.

出版信息

Sensors (Basel). 2023 Jan 9;23(2):756. doi: 10.3390/s23020756.

Abstract

The combination of advanced radar sensor technology and smart grid has broad prospects. It is meaningful to monitor the respiration and heartbeat of grid employees under resting state through radar sensors to ensure that they are in a healthy working state. Ultra-wideband (UWB) radar sensor is suitable for this application because of its strong penetration ability, high range resolution and low average power consumption. However, due to weak heartbeat amplitude and measurement noise, the accurate measurement of the target heart rate is a challenge. In this paper, singular spectrum analysis (SSA) is proposed to reconstruct the eigenvalues of noisy vital signs to eliminate noise peaks around the heartbeat rate; combined with the variational modal decomposition (VMD), the target vital signs can be extracted with high accuracy. The experiment confirmed that the target vital sign information can be extracted with high accuracy from ten subjects at different distances, which can play an important role in short distance human detection and vital sign monitoring.

摘要

先进的雷达传感器技术与智能电网相结合具有广阔的前景。通过雷达传感器监测电网员工静息状态下的呼吸和心跳,以确保他们处于健康的工作状态,这具有重要意义。超宽带(UWB)雷达传感器因其强大的穿透能力、高距离分辨率和低平均功耗而适用于此应用。然而,由于心跳幅度较弱和测量噪声,准确测量目标心率是一项挑战。本文提出奇异谱分析(SSA)来重构有噪声生命体征的特征值,以消除心率周围的噪声峰值;结合变分模态分解(VMD),可以高精度提取目标生命体征。实验证实,在不同距离下可从十名受试者中高精度提取目标生命体征信息,这在短距离人体检测和生命体征监测中可发挥重要作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e04/9861067/0bf02bc45f70/sensors-23-00756-g001.jpg

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