Indoor positioning tracking with magnetic field and improved particle filter

Abstract

The indoor magnetic field is omnipresent and independent from external equipment. Local magnetic field is also relatively stable compared with WiFi signals in the same environment and nonuniform in different locations. However, it has low discernibility, in that there are similar magnetic features in different areas. Pedestrian movement model is a continuous navigation method based on inertial sensors. However, inertial sensors provide only short-term accuracy and suffer from accumulation error. Hence, an indoor positioning tracking that uses the magnetic field and an improved particle filter is proposed in this article. First, adaptive four-threshold step-detection and mixed adaptive step length methods are used to obtain the travel distance in different walking states. Furthermore, an improved particle filter is adopted to calibrate the pedestrian movement model by fusing indoor magnetic field information. Besides, initial locations of particles are restricted in a determined area according to WiFi signals, and the diversity of the particles is increased by a classified heuristic resampling. The proposed system was implemented on an Android phone and extensive experiments were conducted in real indoor environments. The experiments show that the positioning accuracy and system robustness are greatly improved compared with other methods.