MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone

Abstract

Geomagnetic positioning technology has proven to be worth investigating in the field of location-based services (LBSs), but the positioning of geomagnetic technology alone will generate a certain amount of error. To overcome the ambiguity of single-point geomagnetic data, we developed a geomagnetic indoor navigation system, magnetic + particle filters + pedestrian dead reckoning (MagPP) based on the pedestrian dead reckoning (PDR) algorithm using a smartphone as a hardware platform. We calculate the measurement trajectory contour of the PDR to solve the gross error of the magnetic field sequence (MFS). The mean square error criterion of the matching trajectory is established, and the particle filter (PF) algorithm is used to realize the iterative calculation of the real-time correction of the PDR cumulative error. In the test, with an area of 68 × 1.8 m2, the experimental results produced an average positioning error of 1.13 m and a maximum positioning error of 2.17 m. The positioning of the fusion algorithm proposed in this paper is 42% higher than that of the PDR algorithm alone. Compared with the single geomagnetic fingerprint-matching algorithm for indoor positioning, the positioning accuracy is improved by 57%. Therefore, the MagPP algorithm significantly improved indoor positioning.