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Entropy 2019, 21(3), 327; https://doi.org/10.3390/e21030327

Unsupervised Indoor Positioning System Based on Environmental Signatures

1
Key Lab of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, China
2
Department of Computer and Information Sciences, Dire-Dawa Institute of Technology, Dire Dawa 3000, Ethiopia
*
Author to whom correspondence should be addressed.
Received: 17 January 2019 / Revised: 21 March 2019 / Accepted: 22 March 2019 / Published: 26 March 2019
(This article belongs to the Section Information Theory, Probability and Statistics)
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Abstract

Mobile sensors are widely used in indoor positioning in recent years, but most methods require cumbersome calibration for precise positioning results, thus the paper proposes a new unsupervised indoor positioning (UIP) without cumbersome calibration. UIP takes advantage of environment features in indoor environments, as some indoor locations have their signatures. UIP considers these signatures as the landmarks, and combines dead reckoning with them in a simultaneous localization and mapping (SLAM) frame to reduce positioning errors and convergence time. The test results prove that the system can achieve accurate indoor positioning, which highlights its prospect as an unconventional method of indoor positioning. View Full-Text
Keywords: indoor positioning; unsupervised positioning; SLAM; mobile sensor indoor positioning; unsupervised positioning; SLAM; mobile sensor
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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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Feng, P.; Qin, D.; Zhao, M.; Guo, R.; Berhane, T.M. Unsupervised Indoor Positioning System Based on Environmental Signatures. Entropy 2019, 21, 327.

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