摘要
Stepped-frequency continuous-wave (SFCW) radar has emerged as a promising technology for noncontact vital sign monitoring, particularly in multisubject scenarios. Compared to other radar modalities, SFCW provides fine range resolution and stable phase response, making it well-suited for capturing subtle physiological movements in complex environments. In this work, we propose a respiration rate detection framework using a multiple-input multiple-output (MIMO) SFCW radar system. The core of our method is a jointly optimized spatial filter, derived from a constrained optimization problem, which suppresses interference in both angular and distance domains. The solution is obtained using the Lagrange multiplier method, enabling efficient and robust spatial filtering. To enhance signal robustness, we further introduce a 3-channel spatial diversity strategy that leverages not only the target's direct path but also two neighboring spatial channels selected based on the physical chest width. This design helps mitigate spatial ambiguity and improve signal quality in the presence of multiple subjects. The filtered signals are then transformed using the Fourier transform to estimate the respiratory frequencies. Experimental results on a public radar dataset validate the effectiveness of the proposed approach, demonstrating lower estimation errors and improved multisubject separation performance compared to existing methods.