Simultaneous Detection of Multi-Target Vital Signs Using EEMD Algorithm Based on FMCW Radar

Guan-Wei Fang, Ching-Yao Huang, Chin-Lung Yang
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引用次数: 16

Abstract

This paper presents a novel approach to simultaneously monitor multi-target vital-signs using a frequency modulation continuous wave (FMCW) radar within the resolution limitation. For a traditional system architecture on multi-target vital-signs monitoring, complicated systems are required such as phased array radar or continuous wave (CW) radar with beamforming technology. In contrast, this architecture has the advantage of enhanced resolution capability, relatively simple circuit, and low cost. By using advanced signal processing such as adaptive boundary, we can detect multi-target vital signs even though the difference of the distances to the two targets is less than the range resolution of FMCW radar. In terms of demodulation, heart rate (HR) is susceptible to the harmonic of respiratory rate (RR) using complex signal demodulation (CSD). Therefore, this paper uses an ensemble empirical mode decomposition (EEMD) algorithm to extract the intrinsic mode functions of RR and HR. Experiments show that, we can improve signal-to-noise ratio (SNR) and accuracy significantly using this algorithm. And the vital sign errors of the two targets separated at 70 cm and 50 cm are averagely 2.35% and 4.44%, respectively.
基于FMCW雷达的EEMD算法多目标生命体征同时检测
提出了一种在分辨率限制下,利用调频连续波(FMCW)雷达同时监测多目标生命体征的新方法。传统的多目标生命体征监测系统架构,需要采用相控阵雷达或波束形成技术的连续波雷达等复杂系统。相比之下,该架构具有分辨率增强、电路相对简单、成本低等优点。利用自适应边界等先进的信号处理技术,即使两个目标的距离差小于FMCW雷达的距离分辨率,也能检测到多目标的生命体征。在解调方面,采用复信号解调(CSD)时,心率(HR)易受呼吸频率(RR)谐波的影响。因此,本文采用集成经验模态分解(EEMD)算法提取RR和HR的内禀模态函数。实验表明,该算法可以显著提高信噪比和精度。在70 cm和50 cm处分离的两个目标的生命体征误差平均分别为2.35%和4.44%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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