Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Hei Man Chan; Chi-Wai Chow; Li-Sheng Hsu; Yang Liu; Ching-Wei Peng; Yin-He Jian; Chien-Hung Yeh

doi:10.1109/JPHOT.2022.3212628

IEEE Photonics Journal (Jan 2022)

Utilizing Lighting Design Software for Simulation and Planning of Machine Learning Based Angle-of-Arrival (AOA) Visible Light Positioning (VLP) Systems

Hei Man Chan,
Chi-Wai Chow,
Li-Sheng Hsu,
Yang Liu,
Ching-Wei Peng,
Yin-He Jian,
Chien-Hung Yeh

Affiliations

Hei Man Chan: Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Chi-Wai Chow: ORCiD; Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Li-Sheng Hsu: Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Yang Liu: ORCiD; Philips Electronics Ltd., Shatin, Hong Kong
Ching-Wei Peng: Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Yin-He Jian: Department of Photonics & Graduate Institute of Electro-Optical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
Chien-Hung Yeh: ORCiD; Department of Photonics, Feng Chia University, Taichung, Taiwan

DOI: https://doi.org/10.1109/JPHOT.2022.3212628
Journal volume & issue: Vol. 14, no. 6
pp. 1 – 7

Abstract

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We propose to utilize a commercially available DIALux lighting design software for simulation and planning of machine learning (ML) based angle-of-arrival (AOA) visible light positioning (VLP) systems. Here, different ML models, for example, second order linear regression (LR), artificial neural-network (ANN), and convolutional neural-network (CNN) are employed. The proposed VLP simulator works well with different ML algorithms. The results show that the proposed scheme can acts as an effective indoor VLP planning and design tool. Besides, it may also alleviate the training data collection in ML based VLP systems.

Published in IEEE Photonics Journal

ISSN: 1943-0655 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Engineering (General). Civil engineering (General): Applied optics. Photonics; Science: Physics: Optics. Light
Website: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4563994

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