An improved particle filter indoor fusion positioning approach based on Wi-Fi/ PDR/ geomagnetic field

Abstract

The existing indoor fusion positioning methods based on Pedestrian Dead Reckoning (PDR) and geomagnetic technology have the problems of large initial position error, low sensor accuracy, and geomagnetic mismatch. In this study, a novel indoor fusion positioning approach based on the improved particle filter algorithm by geomagnetic iterative matching is proposed, where Wi-Fi, PDR, and geomagnetic signals are integrated to improve indoor positioning performances. One important contribution is that geomagnetic iterative matching is firstly proposed based on the particle filter algorithm. During the positioning process, an iterative window and a constraint window are introduced to limit the particle generation range and the geomagnetic matching range respectively. The position is corrected several times based on geomagnetic iterative matching in the location correction stage when the pedestrian movement is detected, which made up for the shortage of only one time of geomagnetic correction in the existing particle filter algorithm. In addition, this study also proposes a real-time step detection algorithm based on multi-threshold constraints to judge whether pedestrians are moving, which satisfies the real-time requirement of our fusion positioning approach. Through experimental verification, the average positioning accuracy of the proposed approach reaches 1.59 m, which improves 33.2% compared with the existing particle filter fusion positioning algorithms.