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Electronics 2019, 8(1), 63; https://doi.org/10.3390/electronics8010063

Fingerprint-Based Indoor Positioning System Using Visible Light Communication—A Novel Method for Multipath Reflections

Robotics and Mechatronics Lab, University of Ulsan, Ulsan 44610, Korea
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Received: 30 November 2018 / Revised: 24 December 2018 / Accepted: 31 December 2018 / Published: 6 January 2019
(This article belongs to the Special Issue Visible Light Communication and Positioning)
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Abstract

A highly accurate indoor positioning under the effect of multipath reflections has been a prominent challenge for recent research. This paper proposes a novel indoor visible light communication (VLC) positioning model by connecting k-nearest neighbors (kNN) and random forest (RF) algorithms for reflective environments, namely, kNN-RF. In this fingerprint-based model, we first adopt kNN as a powerful solution to expand the number of input features for RF. Next, the importance rate of these features is ranked and the least effective one(s) may be removed to reduce the computation effort. Next, the training process using the RF algorithm is conducted. Finally, the estimation process is utilized to discover the final estimated position. Our simulation results show that this new approach improved the positioning accuracy, making it nearly five times better than other popular kNN algorithms. View Full-Text
Keywords: indoor positioning system (IPS); visible light communication (VLC); multipath reflections; k-nearest neighbors (kNN); random forest (RF) indoor positioning system (IPS); visible light communication (VLC); multipath reflections; k-nearest neighbors (kNN); random forest (RF)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Tran, H.Q.; Ha, C. Fingerprint-Based Indoor Positioning System Using Visible Light Communication—A Novel Method for Multipath Reflections. Electronics 2019, 8, 63.

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