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Improved Indoor Visible Light Positioning System Using Machine Learning by Ahmed M. M. Abdalmajeed; Mohamed Mahmoud; Abd El-Rahman A. El-Fikky; Heba A. Fayed; Moustafa H. Aly is a Engineering article available to read on EtoBox.

What is Improved Indoor Visible Light Positioning System Using Machine Learning about?

## 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 Machine (SVM), and Neural Networks (NNs) are used in combination with the LED Received Signal Strength (RSS) and the angle of a steerable laser. Zemax optics studio simulator is used to build a real indoor scene. Orange data mining software is utilized to apply ML techniques. Our numerical findings show that the suggested system outperforms the other RSS Visible Light Communication (VLC)-based models by reducing the localization error by more than 90% in some areas.

Who reads Improved Indoor Visible Light Positioning System Using Machine Learning?

Author

Ahmed M. M. Abdalmajeed; Mohamed Mahmoud; Abd El-Rahman A. El-Fikky; Heba A. Fayed; Moustafa H. Aly

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Physical Sciences)