| 余慧敏,朱姣姿.基于新模态-小波包分解的超宽带雷达生命体征信号去噪算法[J].电子测量与仪器学报,2024,38(3):143-151 |
| 基于新模态-小波包分解的超宽带雷达生命体征信号去噪算法 |
| Ultra-wideband radar vital signs signal denoising algorithmbased on new mode-wavelet packet analysis |
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| DOI： |
| 中文关键词: 超宽带 自适应 集合经验模态分解 小波包分解 生命体征 |
| 英文关键词:UWB adaptive EEMD WPD vital signs |
| 基金项目:国家自然科学基金（62173140）、湖南省自然科学基金（2021JJ30452）项目资助 |
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| 摘要点击次数: 325 |
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| 中文摘要: |
| 超宽带雷达具有高分辨率，穿透能力强，低功耗等优势，工作时人体无需接触任何电极或传感器，可以穿透衣服、废墟等非金属介质在较远的距离内检测人体生命体征信息，在非接触式生命体征检测方面具有很重要的应用价值。由于人类心跳信号容易被呼吸谐波和其他噪声干扰，为了准确提取人体生命体征信号，提出一种基于改进的自适应噪声集合经验模态分解（ICEEMDAN）与小波包分解（WPD）结合的生命体征信号去噪方法。先通过超宽带雷达测量待测者的生命体征，获取人体所在空间位置提取出体表微动信号，对体表振动信号进行补偿与欠采样处理；利用ICEEMDAN-WPD的阈值去噪方法对微动信号进行模态分解，选取合适的模态分量去噪并进行重构，获取人体心跳微动信号的时频特征。实验结果表明，该算法相较于传统的去噪算法将相关系数提高到了0.940 5，信噪比提高到了9.093 8 dB，保留更多的生命体征信息的同时拥有更高的信噪比，可有效应用于生命体征检测领域。 |
| 英文摘要: |
| Ultra-wideband radar has the advantages of high resolution, strong penetration ability, low power consumption, etc. The human body does not need to contact any electrodes or sensors when the ultra-wideband radar is in operation, and it can penetrate through non-metallic media such as clothes and ruins, and detect the information of human vital signs at a long distance. It has an important application value in non-contact vital signs detection. Since the human heartbeat signal is easily interfered by respiratory harmonics and other noises, in order to accurately extract the human vital signs, vital signs signal denoising method based on the combination of improved adaptive noise ensemble empirical modal decomposition (ICEEMDAN) and wavelet packet decomposition (WPD) is proposed. Firstly, we measure the vital signs of the person to be measured by ultra-wideband radar, obtain the spatial location of the human body to extract the micro-motion signals from the body surface, and perform compensation and under-sampling processing for the vibration signals of the body surface; we use the threshold denoising method of ICEEMDAN-WPD to carry out modal decomposition of the micro motion signals, select appropriate modal components for denoising and reconstruction, and obtain the time-frequency characteristics of the micro-motion signals of the human heartbeat. The experimental results show that the algorithm improves the correlation coefficient to 0.9405 and the signal-to-noise ratio to 9.0938 dB compared with the traditional denoising algorithm, which retains more vital signs information and has higher signal-to-noise ratio, and it can be effectively used in the field of vital signs detection. |
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