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Sensors 2018, 18(5), 1598; https://doi.org/10.3390/s18051598

AMID: Accurate Magnetic Indoor Localization Using Deep Learning

School of Computing, Korea Institute of Science and Technology, Daejeon 34141, Korea
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Received: 28 March 2018 / Revised: 11 May 2018 / Accepted: 14 May 2018 / Published: 17 May 2018
(This article belongs to the Collection Positioning and Navigation)
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Abstract

Geomagnetic-based indoor positioning has drawn a great attention from academia and industry due to its advantage of being operable without infrastructure support and its reliable signal characteristics. However, it must overcome the problems of ambiguity that originate with the nature of geomagnetic data. Most studies manage this problem by incorporating particle filters along with inertial sensors. However, they cannot yield reliable positioning results because the inertial sensors in smartphones cannot precisely predict the movement of users. There have been attempts to recognize the magnetic sequence pattern, but these attempts are proven only in a one-dimensional space, because magnetic intensity fluctuates severely with even a slight change of locations. This paper proposes accurate magnetic indoor localization using deep learning (AMID), an indoor positioning system that recognizes magnetic sequence patterns using a deep neural network. Features are extracted from magnetic sequences, and then the deep neural network is used for classifying the sequences by patterns that are generated by nearby magnetic landmarks. Locations are estimated by detecting the landmarks. AMID manifested the proposed features and deep learning as an outstanding classifier, revealing the potential of accurate magnetic positioning with smartphone sensors alone. The landmark detection accuracy was over 80% in a two-dimensional environment. View Full-Text
Keywords: magnetic landmark; deep learning; recurrence plot magnetic landmark; deep learning; recurrence plot
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Lee, N.; Ahn, S.; Han, D. AMID: Accurate Magnetic Indoor Localization Using Deep Learning. Sensors 2018, 18, 1598.

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