Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

Mahmoud, Abdulrahman A.; Ahmad, Zahir; Onyekpe, Uche; Almadani, Yousef; Ijaz, Muhammad; Haas, Olivier C. L.; Rajbhandari, Sujan

doi:10.3390/electronics10233023

Open AccessArticle

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

by

Abdulrahman A. Mahmoud

¹

,

Zahir Ahmad

²,

Uche Onyekpe

³

,

Yousef Almadani

⁴

,

Muhammad Ijaz

⁴

,

Olivier C. L. Haas

⁵

and

Sujan Rajbhandari

^6,*

¹

School of Strategy and Leadership, Faculty of Business and Law, Coventry University, Coventry CV1 5FB, UK

²

School of Computing, Electronics and Mathematics, Coventry University, Coventry CV1 2JH, UK

³

School of Computer and Data Science, York St John University, York YO31 7EX, UK

⁴

Department of Engineering, Engineering and Materials Research Centre, Manchester Metropolitan University, Manchester M15 5JH, UK

⁵

Centre for Future Transport and Cities, Coventry University, Coventry CV1 5FB, UK

⁶

DSP Centre of Excellence, School of Computer Science and Electronic Engineering, Bangor University, Bangor LL57 1UT, UK

^*

Author to whom correspondence should be addressed.

Electronics 2021, 10(23), 3023; https://doi.org/10.3390/electronics10233023

Submission received: 27 September 2021 / Revised: 27 November 2021 / Accepted: 30 November 2021 / Published: 3 December 2021

(This article belongs to the Special Issue 10th Anniversary of Electronics: Related Researches in Electrical and Autonomous Vehicles)

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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.

Keywords: visible light positioning; outdoor positioning; artificial neural network; receiver diversity; receiver tilting; machine learning

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MDPI and ACS Style

Mahmoud, A.A.; Ahmad, Z.; Onyekpe, U.; Almadani, Y.; Ijaz, M.; Haas, O.C.L.; Rajbhandari, S. Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning. Electronics 2021, 10, 3023. https://doi.org/10.3390/electronics10233023

AMA Style

Mahmoud AA, Ahmad Z, Onyekpe U, Almadani Y, Ijaz M, Haas OCL, Rajbhandari S. Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning. Electronics. 2021; 10(23):3023. https://doi.org/10.3390/electronics10233023

Chicago/Turabian Style

Mahmoud, Abdulrahman A., Zahir Ahmad, Uche Onyekpe, Yousef Almadani, Muhammad Ijaz, Olivier C. L. Haas, and Sujan Rajbhandari. 2021. "Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning" Electronics 10, no. 23: 3023. https://doi.org/10.3390/electronics10233023

APA Style

Mahmoud, A. A., Ahmad, Z., Onyekpe, U., Almadani, Y., Ijaz, M., Haas, O. C. L., & Rajbhandari, S. (2021). Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning. Electronics, 10(23), 3023. https://doi.org/10.3390/electronics10233023

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

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