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RSS-based sensor network localization in contaminated Gaussian measurement noise
Technical University, Darmstadt, Germany.
Technical University Darmstadt, Germany.
Technical University Darmstadt, Germany.
IFEN GmbH, Poing, Germany.
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2013 (English)In: IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2013, IEEE , 2013, 121-124 p.Conference paper, Published paper (Refereed)
Abstract [en]

We study received signal strength-based cooperative localization in wireless sensor networks. We assume that the measurement noise fits a contaminated Gaussian model so as to take into account some outlier conditions. In addition, some environment-dependent parameters are assumed to be unknown. We propose an expectation-maximization based algorithm for robust centralized network localization without offline training. As benchmark for comparison, we express the best achievable localization accuracy in terms of the Cramér-Rao bound. Experimental results demonstrate the advantages of the proposed algorithm as compared to some representative algorithms.

Place, publisher, year, edition, pages
IEEE , 2013. 121-124 p.
Keyword [en]
Cooperative localization, Cramér-Rao bound (CRB), expectation-maximization (EM), non-Gaussian noise, received signal strength (RSS)
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-121629DOI: 10.1109/CAMSAP.2013.6714022ISBN: 978-1-4673-3144-9 (print)OAI: oai:DiVA.org:liu-121629DiVA: diva2:857380
Conference
IEEE 5th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), St. Martin, France, Dec 15-18, 2013
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2016-03-11Bibliographically approved

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Fritsche, CarstenGustafsson, Fredrik
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