Improved indoor visible light positioning system using machine learning

Abstract In this study, we present a novel Visible Light Positioning (VLP) method to reduce the localization error in an indoor environment. Machine Learning (ML) methods including Decision Tree (DT), Support Vector (SVM), and Neural Networks (NNs) are used combination with LED Received Signal Strength (RSS) angle of steerable laser. Zemax optics studio simulator is build real scene. Orange data mining software utilized apply ML techniques. Our numerical findings show that suggested system outperforms other RSS Communication (VLC)-based models by reducing more than 90% some areas.