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 journalArticlepeer-review

1 Citation (Scopus)
70 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 languageEnglish
Article number3023
Number of pages15
JournalElectronics
Volume10
Issue number23
DOIs
Publication statusPublished - 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

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