Outdoor Visible Light Positioning Using Artificial Neural Networks for Autonomous Vehicle Application

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Abstract

In this paper, a novel outdoor 2-D vehicular visible light positioning (VLP) using a linear array of streetlights and artificial neural network (ANN) is proposed. The classical position methods which are mostly based on triangulation will not work with the linear array of the street light. Hence, we proposed a spatial diversity receiver with ANN to overcome the collinearity condition. The proposed system is simulated for a realistic outdoor condition and provides an accurate positioning with an average RMS error of 0.53m.
Original languageEnglish
Title of host publication2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)
PublisherIEEE
Pages1-4
Number of pages4
ISBN (Electronic)9781728167435
ISBN (Print)9781728160511
DOIs
Publication statusPublished - 10 Nov 2020
Event12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020 - Porto, Portugal
Duration: 20 Jul 202022 Jul 2020

Conference

Conference12th International Symposium on Communication Systems, Networks and Digital Signal Processing, CSNDSP 2020
Country/TerritoryPortugal
CityPorto
Period20/07/2022/07/20

Bibliographical note

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Funding Information:
This work is supported by Petroleum Technology Development Fund (PTDF).

Keywords

  • Artificial neural network
  • outdoor positioning
  • receiver diversity
  • visible light positioning

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Signal Processing

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