A Vital Signs Denoising Algorithm for FMCW Radar Based on BKA-VMD Combined Wavelet Threshold

Radar can penetrate non-metallic materials such as clothing, quits, and wooden boards, enabling the non-contact detection of vital signs like human respiration and heartbeat. FMCW radar integrates the advantages of the CW radar and UWB radar, offering excellent ranging and velocity measurement capabilities. This makes it has significant application potential in the field of non-contact vital sign detection. During detection, the chest displacements caused by respiration and the heartbeat is relatively small, besides heartbeat signals can be easily interfered by respiratory harmonics and other noises. In this paper, a denoising method for FMCW radar vital signs signals using Black-winged Kite Algorithm (BKA) to optimize the parameters of Variational Modal Decomposition (VMD) and joint wavelet threshold denoising is proposed for the first time. Experimental results indicate that when the SNR is 0, the algorithm is compared with the EMD joint wavelet thresholding algorithm, the correlation coefficient improves by 4.36%, the value of RMSE reduces by 0.7606, and the value of SNR improves by 5.342dB, which retains more vital signs information while having higher signal-to-noise ratio, and it can be effectively used in the field of vital signs detection.