Abstract: 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.

Mohamed Aizad Bin Mohamed Ghazali, Aroland Kiring, Lyudmila Mihaylova, Hoe Tung Yew, Seng Kheau Chung and Farrah Wong. “Robust Particle Filter for Accurate WiFi-Based Indoor Positioning in the Presence of Outlier-Corrupted Sensor Data”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.7 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160787

@article{Ghazali2025,
title = {Robust Particle Filter for Accurate WiFi-Based Indoor Positioning in the Presence of Outlier-Corrupted Sensor Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160787},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160787},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {7},
author = {Mohamed Aizad Bin Mohamed Ghazali and Aroland Kiring and Lyudmila Mihaylova and Hoe Tung Yew and Seng Kheau Chung and Farrah Wong}
}