Salman, N, Alsindi, N, Mihaylova, L et al. (1 more author) (2014) Super resolution WiFi indoor localization and tracking. In: Proceedings of the 2014 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2014. 2014 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2014, 08-10 Oct 2014, Bonn. IEEE , 1 - 5. ISBN 9781479973873
Abstract
In this paper, we present a complete framework for accurate indoor positioning and tracking using the 802.11a WiFi network. Channel frequency response is first estimated via the least squares (LS) method using an orthogonal frequency division multiplexing (OFDM) pilot symbol. For accurate time of arrival (ToA) distance estimates in multipath environments, super resolution technique i.e. Multiple Signal Classification (MUSIC) is used which capitalizes on the autocorrelation matrix of the estimated channel frequency response. The estimated distances from the base stations (BSs) are then used in the observation model for particle filter (PF) tracking for which a constant velocity motion model is used, depicting indoor mobile movement. The tracking performance of the combined MUSIC-PF is compared with PF performance when a conventional cross correlator (CC) is used for delay estimates. It is shown via simulation that the MUSIC-PF performance is superior to the CC-PF performance.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | (c) 2014, IEEE. This is an author produced version of a paper published in Proceedings of the 2014 Workshop on Sensor Data Fusion: Trends, Solutions, Applications, SDF 2014. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Localization; tracking; WiFi networks |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Institute of Communication & Power Networks (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 31 Mar 2015 10:01 |
Last Modified: | 20 Jan 2018 14:27 |
Published Version: | http://dx.doi.org/10.1109/SDF.2014.6954723 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/SDF.2014.6954723 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:84018 |