Robust Particle Filter for Accurate WiFi-Based Indoor Positioning in the Presence of Outlier-Corrupted Sensor Data

This study presents a comprehensive evaluation of an outlier-robust particle filter (RPF) designed to improve indoor positioning accuracy in complex environments with substantial measurement noise and outliers. The RPF’s performance is benchmarked against a standard Particle Filter (PF) using both simulated and real-world datasets. Simulation results indicate that the RPF consistently outperforms the PF in indoor positioning particularly when sensor measurements contain out-liers, achieving significant reductions in root mean square error (RMSE) for position, velocity, and acceleration estimation, with improvements of approximately 40.02%, 38.48%, and 65.80%, respectively. Real-world experiments, applying a calibrated log-normal path loss model to Wi-Fi received signal strength (RSS) data, further corroborate the RPF’s effectiveness, demonstrating a 93.61% improvement in positioning accuracy compared to the PF. These findings highlight the RPF’s robustness in delivering high accuracy, especially in environments with measurement outliers, establishing it as a reliable solution for indoor tracking in noisy sensor environments.