Using Received-Signal-Strength (RSS) Pre-Processing and Convolutional Neural Network (CNN) to Enhance Position Accuracy in Visible Light Positioning (VLP)

Hsu, Ling-Yuan; Tsai, Deng-Cheng; Chen, Hei Man; Chang, Yun-Han; Liu, Yang; Chow, Chi‐Wai; Song, Shao-Hua; Yeh, Chien‐Hung

doi:10.1364/ofc.2022.w3i.6

Cited by 5 publications

(1 citation statement)

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“…Recently, machine learning based approaches (e.g., gradient boost tree, kernel ridge regression, etc.) have also been introduced in VLP systems to boost the position solving speed and reduce intensity model based errors [20], [28]- [30] Besides individual VLC and VLP, the integration of VLC and VLP for simultaneous communication and positioning has also been proposed and investigated in the literature [31]- [37]. In addition, hybrid RF and VLC (RF/VLC) systems have been further designed to achieve full-coverage bidirectional communications [38]- [44].…”

Section: Introductionmentioning

confidence: 99%

Recent Progress in Visible Light Positioning and Communication Systems

Zhang

Yang

et al. 2023

IEICE Trans. Commun.

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In this paper, we report the recent progress in visible light positioning and communication systems using light-emitting diodes (LEDs). Due to the wide deployment of LEDs for indoor illumination, visible light positioning (VLP) and visible light communication (VLC) using existing LEDs fixtures have attracted great attention in recent years. Here, we review our recent works on visible light positioning and communication, including image sensor-based VLP, photodetector-based VLP, integrated VLC and VLP (VLCP) systems, and heterogeneous radio frequency (RF) and VLC (RF/VLC) systems.

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