An Inertial Magneto-Inductive Positioning System Based on GWO-PF Algorithm

This article describes the technology and realization of an inertial magneto-inductive positioning system (MPS) with the improved GWO-PF algorithm. The system is implemented with a dual-axis magnetic beacon (MB), a three-axis magnetic sensor, and an inertial measurement unit (IMU). Unfortunately, the performance of the magnetic-based PSs is severely impaired by the attitude errors of the magnetic sensor that is directly obtained from IMU. In this article, 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 (PF) based on the improved grey wolf optimizer (GWO-PF) algorithm is developed to improve the positioning performance proposed of the moving target. The realized prototype exhibits a maximum positioning error lower than 0.15 m for the static target in an indoor environment with a medium area of 8.4 m × 6.5 m. The performance of tracking moving target is verified by simulation and the cumulative probability distribution (CPD) indicates that 99% of positioning errors are lower than 0.83 m.