基于毫米波传感器的呼吸检测与去噪方法研究

利用毫米波传感器测量较远人体目标的呼吸信号时，容易受到环境杂波的干扰，导致信号中含有较多噪声。因此提出了一种新的GA-VMD-WT去噪方法。方法针对呼吸信号的特点，借助排列熵设计适应度函数，采用GA算法优化VMD参数，以获得最优模态分量个数K和惩罚因子α，再用优化得到的VMD参数对噪声信号分解，然后对分解结果小波阈值去噪，最后重建得到去噪信号。该方法不仅避免了VMD分解时出现的过分解问题，并且仿真实验显示，与各传统的去噪算法相比较，信噪比分别提高了8.5025dB，7.6642dB，3.3637dB。实测信号实验结果表明，所提方法去噪效果好，可以保留更多有用信号的信息。

Research on breathe signal detection and denoising method based on millimeter Wave sensor

When millimeter wave sensor is used to measure respiratory signal of distant human body, it is easy to be interfered by environmental clutter, which leads to more noise in the signal. Therefore this paper proposes a new GA-VMD-WT denoising method. The fitness function has been designed according to the characteristics of breathing signal and then, VMD parameters are optimized by GA algorithm using the fitness function.The optimized VMD parameters were used to decompose the noise signal, subsequently. In the end, the denoised signal can be obtain after the wavelet threshold for the decomposition results.The proposed method not only avoids the over-decomposition problem in VMD decomposition, but also improves the SNR by 8.5025dB, 7.6642dB and 3.3637dB, respectively, compared with other traditional denoising algorithms. Experimental results show that the denoising effect of the proposed method is good, and more useful signal information can be retained.