Multi-Target Vital Sign Detection via Reconfigurable Intelligent Surface-Aided SIMO-FMCW Radar

This paper examines a reconfigurable intelligent surface (RIS)-assisted single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar system for detecting vital signs, particularly breath rates, of multiple targets. The proposed method employs manifold optimization (MO) to design RIS phase shifts, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received radar signal. The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is then applied to estimate the targets' vital signs. Simulation results show that the proposed scheme outperforms both the no-RIS scenario with an added singular value decomposition (SVD)-based processing at the radar receiver and a semidefinite relaxation-based RIS design approach, all while maintaining lower computational complexity.