Single UWB Base Station-Assisted Indoor Positioning for Smartphones
Abstract
The ultrawideband (UWB) technology, with its high-precision ranging capability, has emerged as a promising solution for indoor localization in the Internet of Things (IoT) applications, such as navigation in complex buildings. The recent integration of UWB chipsets in commercial smartphones has created new opportunities for ubiquitous indoor positioning services. However, the conventional UWB-based positioning systems require multiple base stations with known locations and time synchronization, resulting in substantial infrastructure costs that hinder large-scale deployment. This article proposes a single UWB base station-assisted indoor positioning system for smartphones that significantly reduces both equipment requirements and installation complexity. The system first characterizes the ranging and angle-of-arrival (AoA) measurement performance of smartphone-embedded UWB transceivers to establish appropriate error models. A Levenberg–Marquardt (LM) optimization algorithm is then developed to estimate the initial user position by fusing UWB measurements with pedestrian dead reckoning (PDR) trajectories derived from smartphone inertial sensors. Subsequently, an extended Kalman filter (EKF) framework tightly couples UWB range observations with PDR outputs to continuously correct accumulated drift errors. Experimental results using commercial smartphones show that the proposed system achieves a mean positioning error of 0.276 m in line-of-sight (LOS) scenarios, outperforming stand-alone PDR and a particle-filter-based PDR/UWB fusion benchmark by up to 74.85 % and 45.24 %, respectively. Robustness and generalizability are further validated across structurally different and challenging indoor environments. In a cluttered office, the system constrains the peak error to within 0.83 m even after traversing severe non-LOS (NLOS) conditions, while in a large underground parking garage with pervasive multipath, it sustains a mean error below 0.5 m. The initial position estimation algorithm attains 10–35 cm accuracy across various geometric configurations. These results validate the feasibility of robust submeter indoor positioning using only a single UWB base station, providing a cost-effective solution for large-scale IoT localization deployments.