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Analysis of a nonsmooth optimization approach to robust estimation
University of Lyon, France.
Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering. University of Calif Berkeley, CA 94720 USA.
2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 66, 132-145 p.Article in journal (Refereed) Published
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Abstract [en]

In this paper, we consider the problem of identifying a linear map from measurements which are subject to intermittent and arbitrarily large errors. This is a fundamental problem in many estimation-related applications such as fault detection; state estimation in lossy networks, hybrid system identification, robust estimation, etc. The problem is hard because it exhibits some intrinsic combinatorial features. Therefore, obtaining an effective solution necessitates relaxations that are both solvable at a reasonable cost and effective in the sense that they can return the true parameter vector. The current paper discusses a nonsmooth convex optimization approach and provides a new analysis of its behavior. In particular, it is shown that under appropriate conditions on the data, an exact estimate can be recovered from data corrupted by a large (even infinite) number of gross errors. (C) 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2016. Vol. 66, 132-145 p.
Keyword [en]
Robust estimation; Outliers; System identification; Nonsmooth optimization
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:liu:diva-126801DOI: 10.1016/j.automatica.2015.12.024ISI: 000371099300016OAI: oai:DiVA.org:liu-126801DiVA: diva2:917734
Available from: 2016-04-07 Created: 2016-04-05 Last updated: 2016-04-07

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Ohlsson, Henrik
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Citation style
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