FMCW Radar Vital Signs Detection Based on GMFD Algorithm

To improve the accuracy and stability of vital sign detection in frequency modulated continuous wave (FMCW) radar, a Gaussian Mean Filtering Decomposition (GMFD) algorithm is proposed. This algorithm adopts double symmetric reflection to perform boundary processing on the phase difference signal. Extract the instantaneous mean value of the signal through Gaussian weighted moving average; The intrinsic modal function (IMF) is obtained through difference operation; Control the number of iterations based on the energy ratio threshold; By adding Gaussian white noise multiple times and averaging the decomposition results to enhance stability, finally, the characteristic frequencies are extracted based on the IMF component with the highest energy proportion in the breathing and heartbeat frequency bands to obtain the respiratory rate (RR) and heartbeat rate (HR). The experimental results show that, compared with the Ensemble Empirical Mode Decomposition (EEMD), Complete Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), and variational Mode Extraction (VME) algorithms, the proposed GMFD algorithm has higher accuracy. It demonstrated excellent stability in experimental scenarios where the radar was located at different distances, positions and angles of the subjects.