Vehicular Visible Light Positioning using Receiver Diversity with Machine Learning

Abdulrahman Mahmoud; Zahir Ahmad; Uche Abiola Onyekpe; Yousef Almadani; M. Ijaz; Olivier Haas; Sujan Rajbhandari

doi:10.3390/electronics10233023

Vehicular Visible Light Positioning using Receiver Diversity with Machine Learning

Abdulrahman Mahmoud, Zahir Ahmad, Uche Abiola Onyekpe, Yousef Almadani, M. Ijaz, Olivier Haas, Sujan Rajbhandari

Research output: Contribution to journal › Article › peer-review

6 Citations (Scopus)

124 Downloads (Pure)

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.

Original language	English
Article number	3023
Number of pages	15
Journal	Electronics
Volume	10
Issue number	23
DOIs	https://doi.org/10.3390/electronics10233023
Publication status	Published - 3 Dec 2021

Bibliographical note

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Funding Information:
Funding: This research was partly funded by Petroleum Technology Development Fund (PTDF), Nigeria. OCL Haas was partly funded by Assured CAV parking, innovate-UK grant 105095.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Funder

Petroleum Technology Development Fund

Keywords

artificial neural network
machine learning
outdoor positioning
receiver diversity
receiver tilting
visible light positioning
Receiver diversity
Machine learning
Outdoor positioning
Artificial neural network
Visible light positioning
Receiver tilting
Hardware and Architecture
Computer Networks and Communications
Control and Systems Engineering
Signal Processing
Electrical and Electronic Engineering

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Control and Systems Engineering
Hardware and Architecture
Computer Networks and Communications

Access to Document

10.3390/electronics10233023Licence: CC BY

PublishedFinal published version, 3.13 MBLicence: CC BY

Cite this

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title = "Vehicular Visible Light Positioning using Receiver Diversity with Machine Learning",

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.",

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author = "Abdulrahman Mahmoud and Zahir Ahmad and Onyekpe, {Uche Abiola} and Yousef Almadani and M. Ijaz and Olivier Haas and Sujan Rajbhandari",

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language = "English",

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AU - Mahmoud, Abdulrahman

AU - Ahmad, Zahir

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AU - Ijaz, M.

AU - Haas, Olivier

AU - Rajbhandari, Sujan

N1 - This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Funding Information: Funding: This research was partly funded by Petroleum Technology Development Fund (PTDF), Nigeria. OCL Haas was partly funded by Assured CAV parking, innovate-UK grant 105095. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/12/3

Y1 - 2021/12/3

N2 - 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.

AB - 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.

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KW - receiver diversity

KW - receiver tilting

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KW - Receiver diversity

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KW - Outdoor positioning

KW - Artificial neural network

KW - Visible light positioning

KW - Receiver tilting

KW - Hardware and Architecture

KW - Computer Networks and Communications

KW - Control and Systems Engineering

KW - Signal Processing

KW - Electrical and Electronic Engineering

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Vehicular Visible Light Positioning using Receiver Diversity with Machine Learning

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