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A Novel Signal Separation and De-Noising Technique for Doppler Radar Vital Signal Detection

机译：多普勒雷达生命信号检测的新型信号分离与去噪技术

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摘要

Doppler radar for monitoring vital signals is an emerging tool, and how to remove the noise during the detection process and reconstruct the accurate respiration and heartbeat signals are hot issues in current research. In this paper, a novel radar vital signal separation and de-noising technique based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sample entropy (SampEn), and wavelet threshold is proposed. First, the noisy radar signal was decomposed into a series of intrinsic mode functions (IMFs) using ICEEMDAN. Then, each IMF was analyzed using SampEn to find out the first few IMFs containing noise, and these IMFs were de-noised using the wavelet threshold. Finally, in order to extract accurate vital signals, spectrum analysis and Kullback–Leible (KL) divergence calculations were performed on all IMFs, and appropriate IMFs were selected to reconstruct respiration and heartbeat signals. Moreover, as far as we know, there is almost no previous research on radar vital signal de-noising based on the proposed technique. The effectiveness of the algorithm was verified using simulated and measured experiments. The results show that the proposed algorithm could effectively reduce the noise and was superior to the existing de-noising technologies, which is beneficial for extracting more accurate vital signals.
机译：用于监测生命信号的多普勒雷达是一种新兴工具，如何在检测过程中消除噪声并重建准确的呼吸和心跳信号是当前研究的热点。本文提出了一种新的雷达生命信号分离与降噪技术，该方法基于改进的具有自适应噪声（ICEEMDAN），样本熵（SampEn）和小波阈值的完整集成经验模式分解。首先，使用ICEEMDAN将噪声雷达信号分解为一系列固有模式函数（IMF）。然后，使用SampEn对每个IMF进行分析，以找出包含噪声的前几个IMF，然后使用小波阈值对这些IMF进行去噪。最后，为了提取准确的生命信号，对所有IMF进行了频谱分析和Kullback-Leible（KL）发散计算，并选择了适当的IMF来重建呼吸和心跳信号。而且，据我们所知，基于所提出的技术，几乎没有关于雷达生命信号降噪的研究。通过仿真和实测实验验证了算法的有效性。结果表明，该算法能有效降低噪声，优于现有的降噪技术，有利于提取更准确的生命信号。

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