Received-Signal-Strength (RSS) Based 3D Visible-Light-Positioning (VLP) System Using Kernel Ridge Regression Machine Learning Algorithm with Sigmoid Function Data Preprocessing Method

Yu Chun Wu, Chi Wai Chow*, Yang Liu, Yun Shen Lin, Chong You Hong, Dong Chang Lin, Shao Hua Song, Chien Hung Yeh

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In this work, we propose and demonstrate a received-signal-strength (RSS) based visible-light-positioning (VLP) system using sigmoid function data preprocessing (SFDP) method; and apply it to two types of regression based machine learning algorithms; including the second-order linear regression machine learning (LRML) algorithm, and the kernel ridge regression machine learning (KRRML) algorithm. Experimental results indicate that the use of SFDP method can significantly improve the positioning accuracies in both the LRML and KRRML algorithms. Besides, the SFDP with KRRML scheme outperforms the other three schemes in terms of position accuracy, with the experimental average positioning error of about 2 cm in both horizontal and vertical directions.

Original languageEnglish
Article number9272728
Pages (from-to)214269-214281
Number of pages13
JournalIEEE Access
Volume8
DOIs
StatePublished - 2020

Keywords

  • light-emitting-diode (LED)
  • machine learning
  • Visible light communication (VLC)
  • visible light positioning (VLP)

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