TY - JOUR

T1 - Vehicular Visible Light Positioning using Receiver Diversity with Machine Learning

AU - Mahmoud, Abdulrahman

AU - Ahmad, Zahir

AU - Onyekpe, Uche Abiola

AU - Almadani, Yousef

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.

KW - artificial neural network

KW - machine learning

KW - outdoor positioning

KW - receiver diversity

KW - receiver tilting

KW - visible light positioning

KW - Receiver diversity

KW - Machine learning

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

U2 - 10.3390/electronics10233023

DO - 10.3390/electronics10233023

M3 - Article

VL - 10

JO - Electronics (Switzerland)

JF - Electronics (Switzerland)

SN - 2079-9292

IS - 23

ER -