Vehicular visible light positioning using receiver diversity with machine learning

Mahmoud, Abdulrahman A. and Ahmad, Zahir and Onyekpe, Uche and Almadani, Yousef and Ijaz, Muhammad and Haas, Olivier C. L. and Rajbhandari, Sujan (2021) Vehicular visible light positioning using receiver diversity with machine learning. Electronics, 10 (23). 3023. ISSN 2079-9292 (https://doi.org/10.3390/electronics10233023)

[thumbnail of Mahmoud-etal-Electronics-2023-Vehicular-visible-light-positioning-using-receiver-diversity]
Preview
 Text. Filename: Mahmoud-etal-Electronics-2023-Vehicular-visible-light-positioning-using-receiver-diversity.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

This paper proposes a 2-D vehicular visible light positioning (VLP) system using existing streetlights and diversity receivers. Due to the linear arrangement of streetlights, traditional positioning techniques based on triangulation or similar algorithms fail. Thus, in this work, we propose a spatial and angular diversity receiver with machine learning (ML) techniques for VLP. It is shown that a multi-layer neural network (NN) with the proposed receiver scheme outperforms other machine learning (ML) algorithms and can offer high accuracy with root mean square (RMS) error of 0.22 m and 0.14 m during the day and night time, respectively. Furthermore, the NN shows robustness in VLP across different weather conditions and road scenarios. The results show that only dense fog deteriorates the performance of the system due to reduced visibility across the road.

ORCID iDs

Mahmoud, Abdulrahman A., Ahmad, Zahir, Onyekpe, Uche, Almadani, Yousef, Ijaz, Muhammad, Haas, Olivier C. L. and Rajbhandari, Sujan ORCID logoORCID: https://orcid.org/0000-0001-8742-118X;
CORE (COnnecting REpositories)