UWTrack: Clustering-Assisted Multiperson Passive Indoor Tracking via IR-UWB

Abstract

Device-free passive indoor human tracking based on radio frequency (RF) signals has prompted extensive research in academia and industry. Most existing approaches track a single target due to the coarse-grained spatial resolution. Multiperson tracking methods face challenges in complex and dynamic scenes, leading to a sharp decline in accuracy and an exponential increase in computation. In this article, we employ the impulse radio ultrawideband (IR-UWB), known for its high spatial resolution and range accuracy, to break down these limitations. We design, implement, and evaluate UWTrack, a clustering-assisted device-free tracking system that can achieve the trajectories of multiple persons in real time with two key components. First, a multiperson detection scheme with the adaptive motion filter and the range compensation is devised, which significantly improves the ranging accuracy and reduces false detections. Second, we propose a clustering-based multiperson tracking method to remove noise points and decline extra updating operations in the Gaussian mixture probability hypothesis density (GM-PHD) filter. It makes UWTrack enhance the real-time tracking performance and decrease the computational cost. We conduct experiments to evaluate UWTrack under various scenarios and conditions. The results reveal that UWTrack can achieve 35.3 cm average tracking accuracy and 90.25% detection accuracy in real time, outperforming the existing solutions by more than 36%.