Error compensation in indoor positioning systems based on software defined visible light communication

Currently, a high percentage of the world’s population lives in urban areas, and this proportion will increase in the coming decades. In this context, indoor positioning systems (IPSs) have been a topic of great interest for researchers. On the other hand, Visible Light Communication (VLC) systems have advantages over RF technologies; for instance, they do not need satellite signals or the absence of electromagnetic interference to achieve positioning. Nowadays, in the context of Indoor Positioning (IPS), Visible Light Positioning (VLP) systems have become a strong alternative to RF-based systems, allowing the reduction in costs and time to market. This paper shows a low cost VLP solution for indoor systems. This includes multiple programmable beacons and a receiver which can be plugged to a smartphone running a specific app. The position information will be quickly and securely available through the interchange between the receiver and any configurable LED-beacon which is strategically disposed in an area. The implementation is simple, inexpensive, and no direct communication with any data server is required.

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Indoor Positioning Systems Based on Visible Light Communication: State of the Art

IEEE Communications Surveys & Tutorials ◽

10.1109/comst.2017.2743228 ◽

2017 ◽

Vol 19 (4) ◽

pp. 2871-2893 ◽

Cited By ~ 99

Author(s):

Junhai Luo ◽

Liying Fan ◽

Husheng Li

Keyword(s):

Visible Light ◽

State Of The Art ◽

Visible Light Communication ◽

Indoor Positioning ◽

Positioning Systems

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Hardware and software co-design of visible light indoor positioning systems for robotics

Advanced Control, Automation and Robotics ◽

10.2495/acar140261 ◽

2015 ◽

Cited By ~ 1

Author(s):

F.Y. Che ◽

B. Hussain ◽

K.J. Qiu ◽

M. Liu ◽

L. Wu ◽

...

Keyword(s):

Visible Light ◽

Indoor Positioning ◽

Positioning Systems

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Improved Visible Light-Based Indoor Positioning System Using Machine Learning Classification and Regression

Applied Sciences ◽

10.3390/app9061048 ◽

2019 ◽

Vol 9 (6) ◽

pp. 1048 ◽

Cited By ~ 8

Author(s):

Huy Tran ◽

Cheolkeun Ha

Keyword(s):

Machine Learning ◽

Visible Light ◽

Noise Reduction ◽

Indoor Positioning ◽

Computational Time ◽

Dual Function ◽

Positioning System ◽

Positioning Accuracy ◽

Positioning Systems ◽

Machine Learning Classification

Recently, indoor positioning systems have attracted a great deal of research attention, as they have a variety of applications in the fields of science and industry. In this study, we propose an innovative and easily implemented solution for indoor positioning. The solution is based on an indoor visible light positioning system and dual-function machine learning (ML) algorithms. Our solution increases positioning accuracy under the negative effect of multipath reflections and decreases the computational time for ML algorithms. Initially, we perform a noise reduction process to eliminate low-intensity reflective signals and minimize noise. Then, we divide the floor of the room into two separate areas using the ML classification function. This significantly reduces the computational time and partially improves the positioning accuracy of our system. Finally, the regression function of those ML algorithms is applied to predict the location of the optical receiver. By using extensive computer simulations, we have demonstrated that the execution time required by certain dual-function algorithms to determine indoor positioning is decreased after area division and noise reduction have been applied. In the best case, the proposed solution took 78.26% less time and provided a 52.55% improvement in positioning accuracy.

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