Robust particle filter for accurate WiFi-based indoor positioning in the presence of outlier-corrupted sensor data

This paper 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 outliers, 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 lognormal 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.