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Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

Mahmoud, Abdulrahman A. ORCID: https://orcid.org/0000-0002-1617-0579, Ahmad, Zahir, Onyekpe, Uche ORCID: https://orcid.org/0000-0001-8033-9394, Almadani, Yousef ORCID: https://orcid.org/0000-0001-8190-1684, Ijaz, Muhammad ORCID: https://orcid.org/0000-0002-0050-9435, Haas, Olivier C. L. ORCID: https://orcid.org/0000-0002-4665-2894 and Rajbhandari, Sujan ORCID: https://orcid.org/0000-0001-8742-118X (2021) Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning. Electronics, 10 (23). p. 3023.

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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 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.

Item Type: Article
Status: Published
DOI: https://doi.org/10.3390/electronics10233023
School/Department: School of Science, Technology and Health
URI: http://ray.yorksj.ac.uk/id/eprint/5779

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