Cognitive Indoor Positioning Using Sparse Visible Light Source

Liu, Xiangyu1; Gao, Yujing2; Wang, Xiaojie3; Guo, Lei3,4; Wei, Xuetao1

2022-09-01

10.1109/TCSS.2022.3203996

IEEE Transactions on Computational Social Systems   Impact Factor & Quartile

2329-924X

Big data and cognitive computing have a wide range of applications in smart homes, smart cities, artificial intelligence, and computational social systems. Visible light positioning systems have attracted more and more attention as one of the application scenarios of computational social systems. In visible light positioning systems, the light-emitting diodes (LEDs) ceiling layout makes the smartphone usually obtain less than three LEDs in captured images. Due to the lack of necessary positioning information, most scholars combine the inertial measurement unit (IMU) with the modified filter algorithms to achieve positioning under the sparse light source. However, these systems have the following problems: 1) the azimuth angle obtained by the IMU is always not accurate, which decreases the positioning accuracy and 2) during the dynamic positioning process, the system's initial position is difficult to automatically determine. In this article, we propose indoor high-precision visible light positioning under the sparse light source. First, we propose the geometric correction mechanism, which uses ellipse fitting to calibrate the azimuth angle, so as to increase the positioning accuracy for the static system. Then, we build a motion model for the entire positioning process through the unscented particle filter (UPF), which does not need to manually set initial state parameters, due to random generated particles. It can increase the positioning accuracy for the dynamic system. We evaluate our designed system, and the experimental results show that the average azimuth angle error is 2.04 degrees and average positioning error is 8.8 cm, under the sparse light source.

Cognitive computing inertial measurement unit (IMU) unscented particle filter (UPF) visible light positioning

SCI

English

First ; Corresponding

Natural Science Foundation of Guangdong Province[2021A1515011268] ; Guangdong Basic and Applied Basic Research Foundation[2021A1515110958] ; Support Program for Overseas Students to Return to China for Entrepreneurship and Innovation["cx2021003","cx2021053"] ; Natural Science Foundation of Chongqing[cstc2021jcyj-msxmX0031] ; Chongqing University Innovation Research Group[CXQT21019] ; Chongqing Talents Project[CQYC201903048]

Computer Science

Computer Science, Cybernetics ; Computer Science, Information Systems

WOS:000854577800001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Web of Science

1.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Shenzhen 518055, Peoples R China
2.Northeastern Univ, Sch Comp Sci & Engn, Shenyang 110819, Peoples R China
3.Chongqing Univ Posts & Telecommun, Sch Commun & Informat Engn, Chongqing 400065, Peoples R China
4.Hangzhou Inst Adv Technol, Hangzhou 310058, Peoples R China

Liu, Xiangyu,Gao, Yujing,Wang, Xiaojie,et al. Cognitive Indoor Positioning Using Sparse Visible Light Source[J]. IEEE Transactions on Computational Social Systems,2022,PP(99):1-11.

Liu, Xiangyu,Gao, Yujing,Wang, Xiaojie,Guo, Lei,&Wei, Xuetao.(2022).Cognitive Indoor Positioning Using Sparse Visible Light Source.IEEE Transactions on Computational Social Systems,PP(99),1-11.

Liu, Xiangyu,et al."Cognitive Indoor Positioning Using Sparse Visible Light Source".IEEE Transactions on Computational Social Systems PP.99(2022):1-11.