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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00357709" target="_blank" >RIV/68407700:21230/22:00357709 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.3390/s22082879" target="_blank" >https://doi.org/10.3390/s22082879</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s22082879" target="_blank" >10.3390/s22082879</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20201 - Electrical and electronic engineering

Project

—

Continuities

R - Projekt Ramcoveho programu EK

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-8220

e-ISSN

1424-8220

Volume of the periodical

22

Issue of the periodical within the volume

April

Country of publishing house

CH - SWITZERLAND

Number of pages

21

Pages from-to

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UT code for WoS article

000785138100001

EID of the result in the Scopus database

2-s2.0-85127707242