Multi Person Localization and Breathing Rate Estimation Using Translational and Rotational Synthetic Aperture Radar

Reliable detection and localization of humans through structural obstructions is critical for time-sensitive applications such as disaster response. This paper presents a novel ultra-wideband (UWB) radar-based framework for multi-person localization and breathing rate estimation in obstructed environments. To overcome the challenges posed by signal attenuation, multipath interference, and limited spatial resolution, we propose a synthetic aperture radar (SAR) system that combines linear translation and on-axis rotation of a single radar. By fusing intensity maps from multiple radar viewpoints, the system achieves enhanced localization of human targets. A signal quality assessment pipeline, including SNR-based filtering, dominance ratio analysis, and periodicity checks, is used to extract clean respiratory signals from selected range bins. The estimates are further integrated into a spatial breathing-rate matrix, which is fused with radar motion maps to suppress false detections and improve reliability. The proposed algorithm is validated experimentally under line-of-sight, through-wall, and emulated rubble conditions. Results demonstrate significant improvements in localization accuracy and robustness of respiration-based human presence detection. The proposed post-processing filtering algorithms reduce the 80th percentile Chamfer distance by 1.53×, while the maximum Chamfer distance is reduced by ∼ 3×. The estimated breathing rate correlates strongly with ground truth while achieving a Pearson correlation of 0.79.