Analysis of a nonsmooth optimization approach to robust estimation
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 identi...
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| Published in | Automatica (Oxford) Vol. 66; pp. 132 - 145 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.04.2016
Elsevier |
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| Online Access | Get full text |
| ISSN | 0005-1098 1873-2836 1873-2836 |
| DOI | 10.1016/j.automatica.2015.12.024 |
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| Abstract | 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. |
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| AbstractList | 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. 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. In this paper, we consider the problem of identifying a linear map from measurements which are subject to intermittent and arbitarily 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. |
| Author | Bako, Laurent Ohlsson, Henrik |
| Author_xml | – sequence: 1 givenname: Laurent surname: Bako fullname: Bako, Laurent email: laurent.bako@ec-lyon.fr organization: Laboratoire Ampère, Ecole Centrale de Lyon, Université de Lyon, France – sequence: 2 givenname: Henrik surname: Ohlsson fullname: Ohlsson, Henrik email: ohlsson@berkeley.edu organization: Department of Electrical Engineering, Linköping University, SE-581 83 Linköping, Sweden |
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| Keywords | Outliers Robust estimation Nonsmooth optimization System identification outliers system identification nonsmooth optimization robust estimation |
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| SubjectTerms | Automatic Combinatorial analysis Engineering Sciences Error analysis Hybrid systems Mathematical analysis Nonsmooth optimization Optimization Outliers Permissible error Robust estimation State estimation System identification Vectors (mathematics) |
| Title | Analysis of a nonsmooth optimization approach to robust estimation |
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