Author(s)

Xiaolong Zhang¹, Tao Zhou¹, Jing Wang¹, Tao Wang¹, Hui Zhao²

¹State Grid Shanxi Province Electric Power Company Yangquan Power Supply Company, Yangquan, China.
²School of Automation, Beijing Information Science and Technology University, Beijing, China.

The current particle filtering map matching algorithm has problems such as low map utilization and poor accuracy of turnoff positioning, etc. This paper proposed an improved particle filtering-based map-matching algorithm for the inertial positioning of personnel. The historical moment position constraint and feasible region constraint of particles were introduced in this paper. A resampling method based on multi-stage backtracking of particles was proposed. Therefore, the effectiveness of newly generated particles could be guaranteed. The utilization rate of map information could be improved, thus enhancing the accuracy of personnel localization. The walking experiment results showed that, compared with the traditional PDR algorithm, the proposed method had higher localization accuracy and better repeatability of the localization trajectory for multi-turn paths. Under the total travel of 480 meters, the deviation of the starting end point was less than 2 meters, which was about 0.4% of the total travel.

Personal Positioning, Inertial Navigation, Dead Reckoning, Map Matching, Particle Filtering

Zhang, X. , Zhou, T. , Wang, J. , Wang, T. and Zhao, H. (2023) An Improved Particle Filter Map Matching Algorithm for Personal Inertial Positioning. Journal of Computer and Communications, 11, 103-112. doi: 10.4236/jcc.2023.116007.