An Inertial Magneto-Inductive Positioning System Based on GWO-PF Algorithm
Qinghua Li, Li Xinnian, Changhong Wang, Zhenhuan Wang, Fan Wen, Zehui Zhao
Abstract
This paper describes the technology and realization of an inertial magneto-inductive positioning system with the improved GWO-PF algorithm. The system is implemented with a dual-axis magnetic beacon, a three-axis magnetic sensor, and an IMU. Unfortunately, the performance of the magnetic-based positioning systems is severely impaired by the attitude errors of the magnetic sensor that is directly obtained from IMU. In this paper, a positioning method of inertial magneto-inductive is presented to solve the above problem, which is not affected by the attitude errors of the sensors. Furthermore, a particle filter based on the improved grey wolf optimizer algorithm is developed to improve the positioning performance proposed of the moving target. The realized prototype exhibits a maximum positioning error lower than 0.15m for the static target in an indoor environment with a medium area of 8.4m × 6.5m. The performance of tracking moving target is verified by simulation and the cumulative probability distribution indicates that 99% of positioning errors are lower than 0.83m.