Using silicon photovoltaic cells and machine learning and neural network algorithms for visible-light positioning systems
| Autor: | Wahyu Hendra Gunawan, Chong You Hong, Yang Liu, Ke Ling Hsu, Assaidah Adnan, Liang Yu Wei, Chien-Hung Yeh, Yu-Chun Wu, Chi-Wai Chow |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
business.industry Computer science Optical engineering General Engineering Evolutionary algorithm Visible light communication 02 engineering and technology Systems modeling Machine learning computer.software_genre 01 natural sciences Atomic and Molecular Physics and Optics 010309 optics Root mean square 020210 optoelectronics & photonics 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Energy harvesting Algorithm computer |
| Zdroj: | Optical Engineering. 59 |
| ISSN: | 0091-3286 |
| DOI: | 10.1117/1.oe.59.9.096107 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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