Lighthouse: Accurate 802.11 Localization using Directional Beacon Fingerprinting
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|Title:||Lighthouse: Accurate 802.11 Localization using Directional Beacon Fingerprinting||Authors:||Siddig, Abubakr
Bleakley, Chris J.
|Permanent link:||http://hdl.handle.net/10197/7025||Date:||24-Jun-2015||Online since:||2015-09-15T09:50:31Z||Abstract:||In this paper, we propose a novel method for indoor location estimation using an unmodified mobile device (MD) in an IEEE 802.11 wireless local area network. In this method, Directional Beaconing is combined with Fingerprinting whereby the WiFi access points (APs) transmit a narrow beamwidth signal switching between a small number of fixed directions of emission (DOEs) and the MD records the received signal strength indicator (RSSI) for all in-range APs and all DOEs. These observations give a DOE-RSSI signatures at the MD for each AP. The location of the MD is determined using a K-Nearest Neighbor algorithm which finds the best matching DOE-RSSI signature in a pre-stored map. The method improves on previous omnidirectional fingerprinting methods by increasing the number of RSSI maps available per AP. A testbed network was deployed in an office building and its performance was evaluated. The results show a reduction in mean location error from 2.87 m to 1.64 m (43% improvement) for the proposed method compared with conventional omnidirectional RSSI fingerprinting, when using 3 APs in a positioning space of 12 m by 13.5 m.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||IEEE||Copyright (published version):||2015 IEEE||Keywords:||Indoor localization; WLAN; IEEE; 802.11; WiFi; Positioning; Direction of emission; Location estimation||DOI:||10.1109/ICL-GNSS.2015.7217151||Language:||en||Status of Item:||Not peer reviewed||Is part of:||International Conference on Localization and GNSS (ICL-GNSS), 2015||Conference Details:||International Conference on Localization and GNSS, Gothenburg, Sweden, 22 - 24 June, 2015|
|Appears in Collections:||Computer Science Research Collection|
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