Gyroscope-constrained magnetometer PDR/Wi-Fi indoor positioning algorithm - Ruiyi Tang; Chengkai Tian (PLoS ONE, 2025)
To address the issue of low precision in sensor data measured by smartphones, we propose a gyroscope-constrained magnetometer Pedestrian Dead Reckoning (PDR)/Wi-Fi indoor positioning algorithm, focusing on improving the PDR heading angle. We utilize the heading angle constrained by the gyroscope and magnetometer and enhance fingerprint data using Kriging interpolation, effectively doubling the signal fingerprint density. We combine the optimized PDR algorithm and Wi-Fi fingerprint positioning results through an Extended Kalman Filter. Experimental results show that the traditional PDR algorithm has an average positioning error of 2.02 meters, with 90% of errors below 3.71 meters. The improved PDR algorithm reduces the average positioning error to 1.07 meters, with 90% of errors below 2.12 meters. Integrating Wi-Fi and the improved PDR algorithm further reduces the average positioning error to 0.71 meters, with 90% of errors below 1.42 meters.