Expand this Topic clickable element to expand a topic
Skip to content
Optica Publishing Group

Accurate Indoor Visible Light Positioning System utilizing Machine Learning Technique with Height Tolerance

Not Accessible

Your library or personal account may give you access

Abstract

An accurate, low-cost indoor visible light positioning system utilizing machine learning technique is proposed and experimentally demonstrated. The average position resolution of the system can achieve 3.65 cm with height tolerance range of 15 cm.

© 2018 The Author(s)

PDF Article
More Like This
An Indoor Visible Light Communication and Positioning System Based on Machine Learning and Alamouti STBC

Shencheng Ni, Feng Wang, Shuying Han, Xiang Li, Wu Liu, Cai Li, and Shanhong You
SW4L.1 CLEO: Science and Innovations (CLEO:S&I) 2020

Demonstration of high precision 3D indoor positioning system based on two-layer ANN machine learning technique

Jiale He, Chin-Wei Hsu, Qi Zhou, Ming Tang, Songnian Fu, Deming Liu, Lei Deng, and Gee-Kung Chang
Th3I.2 Optical Fiber Communication Conference (OFC) 2019

Using DIALux and Regression-based Machine Learning Algorithm for Designing Indoor Visible Light Positioning (VLP) and Reducing Training Data Collection

Shao-Hua Song, Dong-Chang Lin, Yun-Han Chang, Yun-Shen Lin, Chi-Wai Chow, Yang Liu, Chien-Hung Yeh, Kun-Hsien Lin, Yi-Chang Wang, and Yi-Yuan Chen
Tu5E.3 Optical Fiber Communication Conference (OFC) 2021

Select as filters


Select Topics Cancel
© Copyright 2024 | Optica Publishing Group. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies.