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Sensors 2018, 18(4), 963; https://doi.org/10.3390/s18040963

MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method

Faculty of Computer and Information Science, University of Ljubljana, Vecna pot 113, SI-1000 Ljubljana, Slovenia
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Received: 20 February 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
(This article belongs to the Section Sensor Networks)
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Abstract

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHz Wi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage. View Full-Text
Keywords: adaptive localization; indoor positioning; model-based localization; multi-frequency localization; propagation modeling; IEEE 802.11ah adaptive localization; indoor positioning; model-based localization; multi-frequency localization; propagation modeling; IEEE 802.11ah
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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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Tuta, J.; Juric, M.B. MFAM: Multiple Frequency Adaptive Model-Based Indoor Localization Method. Sensors 2018, 18, 963.

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