WiGig-based Joint Multi-Person Positioning and Respiration Sensing

Wi-Fi sensing has gained significant interest thanks to its low cost and easy deployment. However, existing works in WiGig-based sensing highly rely on prior knowledge such as the location of transmitter and receiver or known relative positions. To address these limitations, the paper presents a prior-knowledge-free approach for multi-person localization and breath rate (BR) estimation using the IEEE 802.11ay standard. This approach accomplishes two key objectives: (1) utilizing the beamforming functionalities specified in IEEE 802.11ay to estimate the line-of-sight (LoS) path, aiding in target localization, and (2) employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm to associate scatterers with their respective targets, enhancing the robustness of BR estimation. Simulation results demonstrate that the proposed method achieves centimeter-level localization and precise BR estimation, all without relying on prior knowledge. This holds true even in harsh scenarios where target locations are close together.