TY - GEN
T1 - A Visible Light Positioning System based on Support Vector Machines
AU - Chaudhary, Neha
AU - Younus, Othman Isam
AU - Nazari Chaleshtori, Zahra
AU - Alves, Luis Nero
AU - Ghassemlooy, Zabih
AU - Zvanovec, Stanislav
N1 - Funding Information:
This work is supported by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 764461.
Funding Information:
ACKNOWLEDGMENT This work is supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement no. 764461 (VisIoN). It is also supported by Northumbria University Ph.D. Scholarship, and CTU project SGS20/166/OHK3/3T/13. It is also based upon work from COST Action CA19111 NEWFOCUS, supported by COST (European Cooperation in Science and Technology).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/9/13
Y1 - 2021/9/13
N2 - 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, ?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 ?p of 0.1, 0.2, and 0.3 m, respectively.
AB - 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, ?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 ?p of 0.1, 0.2, and 0.3 m, respectively.
KW - polynomial regression
KW - RSS.
KW - SVM
KW - visible light positioning
KW - VLC
UR - http://www.scopus.com/inward/record.url?scp=85118437984&partnerID=8YFLogxK
U2 - 10.1109/PIMRC50174.2021.9569249
DO - 10.1109/PIMRC50174.2021.9569249
M3 - Conference contribution
AN - SCOPUS:85118437984
T3 - IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
BT - 2021 IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE Annual International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2021
Y2 - 13 September 2021 through 16 September 2021
ER -