Literature DB >> 31163815

Visible light communication and positioning using positioning cells and machine learning algorithms.

Yu-Cheng Chuang, Zhi-Qing Li, Chin-Wei Hsu, Yang Liu, Chi-Wai Chow.   

Abstract

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 2nd-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 2nd-order regression ML model, while the position error is within 5 cm when using the polynomial trilateral ML model.

Entities:  

Year:  2019        PMID: 31163815     DOI: 10.1364/OE.27.016377

Source DB:  PubMed          Journal:  Opt Express        ISSN: 1094-4087            Impact factor:   3.894


  4 in total

1.  Deep Learning-Based Next-Generation Waveform for Multiuser VLC Systems.

Authors:  Hafiz M Asif; Affan Affan; Naser Tarhuni; Kaamran Raahemifar
Journal:  Sensors (Basel)       Date:  2022-04-04       Impact factor: 3.576

2.  Single LED, Single PD-Based Adaptive Bayesian Tracking Method.

Authors:  Duckyong Kim; Jong Kang Park; Jong Tae Kim
Journal:  Sensors (Basel)       Date:  2022-08-29       Impact factor: 3.847

3.  High-Accuracy Height-Independent 3D VLP Based on Received Signal Strength Ratio.

Authors:  Yihuai Xu; Xin Hu; Yimao Sun; Yanbing Yang; Lei Zhang; Xiong Deng; Liangyin Chen
Journal:  Sensors (Basel)       Date:  2022-09-21       Impact factor: 3.847

4.  Autonomous Fingerprinting and Large Experimental Data Set for Visible Light Positioning.

Authors:  Tyrel Glass; Fakhrul Alam; Mathew Legg; Frazer Noble
Journal:  Sensors (Basel)       Date:  2021-05-08       Impact factor: 3.576

  4 in total

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