Virtual Wireless Device-Constrained Robust Extended Kalman Filters for Smartphone Positioning in Indoor Corridor Environment

Abstract

Indoor positioning technology is the key to achieving seamless indoor/outdoor location-based services (LBSs). As typical nonlinear information, pedestrian accessibility improves the performance of the indoor positioning system. Based on a framework and related positioning algorithms of tightly coupled heterogeneous observables from Wi-Fi/Bluetooth low energy (BLE)/pedestrian dead reckoning (PDR), this work presents a real-time approach for pedestrian accessibility and floor map-constrained hybrid indoor positioning method using virtual wireless devices (VWDs) on smartphones. The nonlinear characteristic of accessibility is transformed into VWDs to constrain the position estimation at the observation level. The evaluation of the proposed technique with three different criteria shows that the accuracy, reliability, and user experience of the estimated locations are comparable to particle filter (PF) results. In a typical corridor environment, the proposed method achieves 0.93 m average positioning accuracy while balancing the computational time consumption.