Development and Evaluation of a Combined WLAN and Inertial Indoor Pedestrian Positioning System

Abstract

In this paper we have presented an indoor positioning system for pedestrians combining Wireless LAN fingerprinting with foot mounted inertial and magnetometer sensors. To achieve a system capable of real-time processing we have employed a hierarchical Bayesian filtering approach using cascaded extended Kalman filters. The accuracy of the combined system was quantitatively evaluated in a real building against ground-truth. Our results show that accuracy is much higher than using Wireless LAN fingerprinting alone; in our experiment we achieved a positioning error (standard deviation) of roughly 1.6 meters. The approach requires only minimal fingerprinting effort since the high accuracy is achieved by the support of the inertial-based step estimation in the overall estimation process.