超宽带雷达是一种重要的生命探测遥感工具,文中利用超宽带雷达穿透能力强、分辨率高等优点,可以得到人体的生命体征信息,处理雷达回波信号可以得到呼吸心跳信息,实现对生命信号的非接触式监测。文中针对回波信号易受环境噪声影响、心跳信号微弱且易受呼吸谐波影响的问题,构造了生命体征模型模拟人体呼吸与心跳频率,提出了一种基于变分模态分解(VMD)与多重分类算法(MUSIC)相结合的方法。使用PulsON440 超宽带雷达在1 m 距离处进行了实验,与传统的快速傅里叶变换、奇异值分解相比,该方法提取的呼吸和心跳信号更加准确。在不同距离和遮蔽条件下验证了该方法的适用性。结果表明提出的基于MUSIC 和VMD 相结合的方法能够有效地从大呼吸信号中分离出小心跳信号,准确地检测出呼吸和心跳频率。

Ultra-wideband radar is an important remote sensing tool for life detection, and this paper uses the advantages of ultrawideband radar with strong penetration and high resolution, which can obtain human vital sign information, and can process radar echo signals to obtain respiratory and heartbeat information, so as to realize non-contact monitoring of life signals. Aiming at the problems that the echo signal is easily affected by environmental noise, the heartbeat signal is weak and easily affected by respiratory harmonics, a vital sign model is constructed to simulate human breathing and heartbeat frequency, and a new method based on variational mode decomposition (VMD) and multiple signal classification (MUSIC). Experiments were carried out using PulsON440 ultra-wideband radar at a distance of 1 meter. Compared with traditional Fast Fourier Transform (FFT) and Singular Value Decomposition (SVD), the method extracts more accurate breathing and heartbeat signals. The applicability of the method is verified under different distances and shading conditions. The results show that the proposed method based on the combination of MUSIC and VMD can effectively separate the small heartbeat signal from the large breathing signal, and accurately detect the breathing and heartbeat frequency.

TN959.6

2021 年度中央引导地方科技发展资金资助项目(YDZJSX2021A007);2020 年度山西省高等学校科技成果转化培育项目(2020CG019);山西省专利转化项目(202405006)