Feasibility of region of interest selection preprocessing using a multi-photodiode fingerprint-based visible light positioning system

Abstract

This research presents a novel Multi-Photodiode Fingerprint-Based Visible Light Positioning (VLP) system aimed at improving the accuracy and reducing the computational expenses of indoor localization. The system leverages an advanced K-Nearest Neighbors (KNN) algorithm, enhanced by Signal Strength Clustering, alongside a region selection strategy based on frequency-modulated VLC encoded IDs. Through extensive simulations, the system demonstrated a notable reduction in Mean Absolute Error (MAE) to approximately 2.5 meters, with a Root Mean Square Error (RMSE) of around 3.0 meters. In addition, the system exhibited robustness across varying ambient light conditions and room sizes, maintaining an accuracy rate of 95%, even in challenging environments. Analysis revealed that error rates increased in larger rooms, with average errors ranging from 1.50 meters in smaller spaces to 3.51 meters in larger environments. This suggests that while the system is effective in smaller areas, its accuracy diminishes slightly as room size expands. However, integrating frequency domain analysis and region of interest (ROI) selection proved to be a practical approach, enhancing the overall performance of the VLP system by providing faster and more accurate indoor navigation. Future research includes exploring advanced modulation techniques integrating supplementary sensing technologies and fine-tuning the algorithm parameters to improve the system’s accuracy and reliability, especially in more complex or dynamic environments.