A radar vital signs detection method in complex environments.

With the growing demand for non-contact monitoring of vital signs such as respiration and heartbeat, frequency-modulated continuous wave (FMCW) radars have emerged as a promising solution for precise analysis of these signals. However, in complex environments such as indoors or inside vehicles, masking effects significantly degrade the accuracy of the target's distance. Additionally, multiple harmonics of the respiration frequency can easily leak into the heartbeat frequency range, resulting in biased heart rate estimation. To address these challenges, we propose the Matrix Coefficient Selection Method (MCSM), a robust distance detection approach that suppresses interference between targets and mitigates the impact of other obstacles in the environment, thereby improving the robustness of distance detection. Inspired by the harmonic mitigation techniques employed in power systems, we propose the Recursive Least Squares Respiratory Harmonic Suppression (RLSRHS) method, which is derived from an improved adaptive filter structure, to suppress respiratory harmonics. Simulation experiments demonstrate that the MCSM method reduces the MAE by approximately 40% at distance detection compared to traditional methods, while the accuracy of heart rate estimation after RLSRHS respiratory harmonic suppression reaches 83%. Extensive actual experiments, compared with contact instruments such as electrocardiogram monitors, Xiaomi wristbands, and respiratory sensors, show that the error is about 4%.