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22 September 2020 Using silicon photovoltaic cells and machine learning and neural network algorithms for visible-light positioning systems
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Abstract

We propose and experimentally demonstrate visible-light positioning (VLP) systems using silicon photovoltaic cells (Si-PVCs) and machine learning and neural network algorithms. Both angle-of-arrival (AOA)-based and received-signal-strength (RSS)-based VLP systems are evaluated and compared. The Si-PVC could also provide energy harvesting to store received optical power for the mobile unit. Here, second-order linear regression machine learning (RML) model and two-layer neural network are implemented in both AOA-based and RSS-based VLP systems to enhance the positioning accuracy. The root mean square (RMS) average positioning error of the AOA-based VLP system is reduced from 7.22 to 3.46 and to 2.99 cm when using the RML and neural network, respectively. The RMS average positioning error of the RSS-based VLP system is reduced from 7.07 to 3.01 and to 2.60 cm when using the RML and neural network, respectively. The experimental results clearly illustrate that the proposed schemes can significantly improve the positioning accuracy.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 0091-3286/2020/$28.00 © 2020 SPIE
Chong-You Hong, Yuchun Wu, Yang Liu, Ke-Ling Hsu, Wahyu Hendra Gunawan, Assaidah Adnan, Liang-Yu Wei, Chien-Hung Yeh, and Chi-Wai Chow "Using silicon photovoltaic cells and machine learning and neural network algorithms for visible-light positioning systems," Optical Engineering 59(9), 096107 (22 September 2020). https://doi.org/10.1117/1.OE.59.9.096107
Received: 22 February 2020; Accepted: 10 September 2020; Published: 22 September 2020
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