Visible Light Positioning for 2D Indoor Environments via the Standard Extreme Learning Machine

This document addresses the problem of indoor positioning, where the Global Positioning System (GPS) presents limitations such as interference, spectrum saturation, and low prediction capacity. As an alternative, a Visible Light Positioning (VLP) system is proposed. An Extreme Learning Machine (ELM) neural network is used to predict the location (x,y) of a receiver (photodiode) in a database constructed in a controlled environment with 7344 samples containing RSS values. The model was trained using 5-fold cross-validation and evaluated with metrics such as root mean square error (RMSE), correlation coefficient (r), and training time. The ELM results achieved an accuracy of 13.50 cm (RMSE) using 200 neurons, with a value of r=0.9971 and a training time of ms, reinforcing its applicability in real-time. Additionally, the results are compared with state-of-the-art models that constructed the database, where the best performance is achieved by the ridge regression approach, with an accuracy of 13.3 cm (RMSE). Therefore, the ELM not only achieves comparable performance but also offers greater computational efficiency, positioning it as a feasible proposal for indoor localization systems.