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-modulatedcontinuous wave (FMCW) radars have emerged as a promising solution for precise analysis of these signals. However, incomplex environments such as indoors or inside vehicles, masking effects significantly degrade the accuracy of the target’sdistance. 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 mitigate the impact of otherobstacles in the environment, thereby improving the robustness of distance detection, particularly in multi-target scenarios.Inspired by the harmonic mitigation techniques employed in power systems, we propose RLSRHS, which is derived froman improved adaptive filter structure, to suppress respiratory harmonics. Simulation experiments demonstrate that theMCSM method reduces the MAE by approximately 40% at distance detection compared to traditional methods, while theRLSRHS method effectively suppresses interference from respiratory harmonics. Extensive real-world experiments, includingcomparisons with ECG monitoring devices, bracelets, and breath sensors, further validate the simulation results, with the errorin heart rate and respiration rate being approximately 4%.