| 摘要: |
| 线性调频连续波（FMCW）雷达能够通过非接触的方式采集人体的呼吸和心跳信号，为了去除和减少生命信号中的杂波干扰，本文提出了基于改进的自适应集合经验模态分解（ICEEMDAN）和长数据序列截取的生命信号分解方法，通过延长观察时间，然后截取时间序列，得到既定观察时间的最终固有模态函数（IMF）分量，通过模糊熵对所有IMF信号进行分析来识别含噪信号，并对含有噪声的IMF信号进行去噪处理，综合分析相关性和能量阈值的结果，挑选出合适的IMF 分量重构生命信号。通过仿真和实测表明，所提出的方法能够大幅减少噪声，优于现有的去噪技术，有利于提高提取的呼吸和心跳信号的精准度和真实性。 |
| 关键词: 线性调频连续波 改进的自适应集合经验模态分解 长数据序列截取 模糊熵 |
| DOI：DOI:10.3969/j.issn.1672-2337.2023.01.013 |
| 分类号:TN957 |
| 基金项目:国家自然科学基金（No. 61771085） |
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| Radar Life Signal Detection Method Based on ICEEMDAN |
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LI Congkang， HUANG Jun， ZHENG Yuanjie
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1. School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China;2. Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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| Abstract: |
| Linear frequency modulated continuous wave （FMCW） radar can collect respiratory and heartbeat signals of human body in a non?contact way. In order to remove and reduce clutter interference in vital signals, this paper proposes a decomposition method of vital signals based on improved complete ensemble empirical mode decomposition with adaptive noise （ICEEMDAN） and long data series interception. And then, intercepting the time series by extending the observation time, the eventually intrinsic mode function （IMF） component of the given observation time is obtained. All IMF signals are analyzed through the fuzzy entropy to identify noise signal, and to deal with the noise of the signal containing noise. The results of correlation and energy threshold are comprehensively analyzed to pick out the right IMF component to reconstruct the life signals. Simulation and experimental results show that the proposed method can greatly reduce noise, superior to the existing denoising techniques, and is beneficial to improve the accuracy and authenticity of extracted respiratory and heartbeat signals. |
| Key words: LFMCW ICEEMDAN long data series interception fuzzy entropy |
