Open AccessVisible light communication and positioning using positioning cells and machine learning algorithms
Author(s) -
Yu Cheng Chuang,
Zhi Qing Li,
Chin Wei Hsu,
Yang Liu,
ChiWai Chow
Publication year - 2019
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.27.016377
Subject(s) - position (finance) , algorithm , computer science , polynomial regression , polynomial , visible light communication , position error , artificial intelligence , regression analysis , optics , machine learning , mathematics , light emitting diode , physics , statistics , calibration , mathematical analysis , finance , economics
We propose and experimentally demonstrate a practical visible light position (VLP) system using repeated unit cells and machine learning (ML) algorithms. ML is employed to increase the positioning accuracy. Algorithms of the 2 nd -order regression ML model and the polynomial trilateral ML model are discussed. More than 80% of the measurement data have position error within 4 cm when using the 2 nd -order regression ML model, while the position error is within 5 cm when using the polynomial trilateral ML model.