An Improved Pedestrian Navigation Method Based on the Combination of Indoor Map Assistance and Adaptive Particle Filter

Abstract

At present, the traditional indoor pedestrian navigation methods mainly include pedestrian dead reckoning (PDR) and zero velocity update (ZUPT), but these methods have the problem of error divergence during long time navigation. To solve this problem, under the condition of not relying on the active sensing information, combined with the characteristics of particles “not going through the wall” in the indoor map building structure, an improved adaptive particle filter (PF) based on the particle “not going through the wall” method is proposed for pedestrian navigation in this paper. This method can restrain the error divergence of the navigation system for a long time. Compared to the traditional pedestrian navigation method, based on the combination of indoor map assistance (MA) and particle filter, a global search method based on indoor MA is used to solve the indoor positioning problem under the condition of the unknown initial position and heading. In order to solve the problem of low operation efficiency caused by the large number of particles in PF, a calculation method of adaptively adjusting the number of particles in the process of particle resampling is proposed. The results of the simulation data and actual test data show that the proposed indoor integrated positioning method can effectively suppress the error divergence problem of the navigation system. Under the condition that the total distance is more than 415.44 m in the indoor environment of about 2600 m2, the average error and the maximum error of the position are less than two meters relative to the reference point.