A Visible Light Positioning System based on Support Vector Machines
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F21%3A00353411" target="_blank" >RIV/68407700:21230/21:00353411 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1109/PIMRC50174.2021.9569249" target="_blank" >https://doi.org/10.1109/PIMRC50174.2021.9569249</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/PIMRC50174.2021.9569249" target="_blank" >10.1109/PIMRC50174.2021.9569249</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
A Visible Light Positioning System based on Support Vector Machines
Popis výsledku v původním jazyce
In this work, a new indoor visible light positioning algorithm is proposed based on support vector machines (SVM) and polynomial regression. Two different multipath environments of an empty room and a furnished room are considered. The algorithm starts by addressing the received power distance relation, considering polynomial regression models fitted to the specific areas of the room. In the second stage, an SVM is used to classify the best-fitted polynomial, which is used with nonlinear least squares to estimate the position of the receiver. The results show that, in an empty room, the positioning accuracy improvement for the positioning error, ε p of 2.5 cm are 36.1, 58.3, and 72.2 % for three different scenarios according to the regions’ distribution in the room. For the furnished room, a positioning relative accuracy improvement of 214, 170, and 100 % is observed for ε p of 0.1, 0.2, and 0.3 m, respectively.
Název v anglickém jazyce
A Visible Light Positioning System based on Support Vector Machines
Popis výsledku anglicky
In this work, a new indoor visible light positioning algorithm is proposed based on support vector machines (SVM) and polynomial regression. Two different multipath environments of an empty room and a furnished room are considered. The algorithm starts by addressing the received power distance relation, considering polynomial regression models fitted to the specific areas of the room. In the second stage, an SVM is used to classify the best-fitted polynomial, which is used with nonlinear least squares to estimate the position of the receiver. The results show that, in an empty room, the positioning accuracy improvement for the positioning error, ε p of 2.5 cm are 36.1, 58.3, and 72.2 % for three different scenarios according to the regions’ distribution in the room. For the furnished room, a positioning relative accuracy improvement of 214, 170, and 100 % is observed for ε p of 0.1, 0.2, and 0.3 m, respectively.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
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OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Proceedings of the 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications
ISBN
978-1-7281-7586-7
ISSN
1558-2612
e-ISSN
—
Počet stran výsledku
6
Strana od-do
—
Název nakladatele
University of Oulu
Místo vydání
Oulu
Místo konání akce
Oulu
Datum konání akce
13. 9. 2021
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
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