Pedestrian navigation fusing inertial and RSS/TOF measurements with adaptive movement/measurement models: Experimental evaluation and theoretical limits

A common approach for advanced Indoor Localization systems is the fusion of complementary techniques, such as inertial navigation systems like pedestrian dead-reckoning (PDR) and absolute measurement methods like radio-frequency (RF) beacon-based positioning. Although this fusion approach provides accurate drift-free absolute positioning, the best results are only obtained if the techniques are adapted to each environment and user. This requires a previous campaign of building calibration and movement model estimation that will be specific to the place and person. In this paper, we tackle this problem by presenting a real-time pedestrian navigation system that fuses PDR and RF beacon-based strategies using a flexible particle filter (PF) implementation, with the following innovative aspects: (1) the definition of an adaptive stride movement model valid for different walking styles, which is used in the PF prediction stage; (2) the dynamic estimation of the measurements model from real-time Received Signal Strength (RSS) and Time of Flight (TOF) values, which is used in the PF update stage; (3) the tracking of the person's position without an initial position/heading nor specific calibration. Additionally, we have obtained the Cramer-Rao lower bound (CRLB) for our fusion-based approach, in order to assess rigorously the performance of the positioning accuracy. We have tested the system in a building fusing PDR with TOF and RSS values coming from WiFi access points or ZigBee nodes. For trajectories with a total length of approximately 1000 m we obtained an error of less than 1.75 m for 90% of the total path length, with both systems. The empirical results match closely the CRLB, showing that our system performs close to the theoretical limit. (C) 2013 Elsevier B.V. All rights reserved.