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Entropy 2019, 21(3), 327;

Unsupervised Indoor Positioning System Based on Environmental Signatures

Key Lab of Electronic and Communication Engineering, Heilongjiang University, Harbin 150080, China
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)
PDF [554 KB, uploaded 28 March 2019]
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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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