The Usage of ANN for Regression Analysis in Visible Light Positioning Systems

Chaudhary, Neha, Younus, Othman, Alves, Luis Nero, Ghassemlooy, Fary and Zvanovec, Stanislav (2022) The Usage of ANN for Regression Analysis in Visible Light Positioning Systems. Sensors, 22 (8). p. 2879. ISSN 1424-8220

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In this paper, we study the design aspects of an indoor visible light positioning (VLP) system that uses an artificial neural network (ANN) for positioning estimation by considering a multipath channel. Previous results usually rely on the simplistic line of sight model with limited validity. The study considers the influence of noise as a performance indicator for the comparison between different design approaches. Three different ANN algorithms are considered, including Levenberg−Marquardt, Bayesian regularization, and scaled conjugate gradient algorithms, to minimize the positioning error (εp) in the VLP system. The ANN design is optimized based on the number of neurons in the hidden layers, the number of training epochs, and the size of the training set. It is shown that, the ANN with Bayesian regularization outperforms the traditional received signal strength (RSS) technique using the non-linear least square estimation for all values of signal to noise ratio (SNR). Furthermore, in the inner region, which includes the area of the receiving plane within the transmitters, the positioning accuracy is improved by 43, 55, and 50% for the SNR of 10, 20, and 30 dB, respectively. In the outer region, which is the remaining area within the room, the positioning accuracy is improved by 57, 32, and 6% for the SNR of 10, 20, and 30 dB, respectively. Moreover, we also analyze the impact of different training dataset sizes in ANN, and we show that it is possible to achieve a minimum εp of 2 cm for 30 dB of SNR using a random selection scheme. Finally, it is observed that εp is low even for lower values of SNR, i.e., εp values are 2, 11, and 44 cm for the SNR of 30, 20, and 10 dB, respectively.

Item Type: Article
Additional Information: Funding information: This work was supported by H2020/MSCA-ITN funding program under the framework of European Training Network on Visible Light Based Interoperability and Networking, project (VisIoN) grant agreement no 764461, Northumbria University Ph.D. Scholarship, EU COST Action NEWFOCUS CA19111
Uncontrolled Keywords: visible light communication (VLC), visible light positioning, multipath reflections, non-linear least square, artificial neural network (ANN), Bayesian regularization
Subjects: F300 Physics
H600 Electronic and Electrical Engineering
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Rachel Branson
Date Deposited: 11 Apr 2022 08:12
Last Modified: 11 Apr 2022 08:15

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