A Magnetic-Aided PDR Localization Method Based on the Hidden Markov Model

Abstract

Pedestrian dead reckoning (PDR) is a promising localization technique since it can be implemented on the widely used smartphones equipped with low cost inertial sensors. However, the PDR localization severely suffers from the positioning errors accumulation. In this paper, a magnetic-aided PDR localization model is proposed to calibrate PDR. The corner is detected by employing magnetic field features and it is matched to one of corners pre-stored offline in the database to get the pedestrian’s current location. Hidden Markov model (HMM) is used to do the matching, which avoids the match mistake and improves the localization precision. The experimental results show that the proposed localization method can improves the positioning accuracy of PDR by 77.56% at most with perfect stability and robustness at the same time.