liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Generalized Kalman smoothing: Modeling and algorithms
University of Washington, USA.
University of Washington, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
IBM TJ Watson Research Centre, NY USA.
Show others and affiliations
2017 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 86, 63-86 p.Article in journal (Refereed) Published
Abstract [en]

State-space smoothing has found many applications in science and engineering. Under linear and Gaussian assumptions, smoothed estimates can be obtained using efficient recursions, for example Rauch Tung Striebel and Mayne Fraser algorithms. Such schemes are equivalent to linear algebraic techniques that minimize a convex quadratic objective function with structure induced by the dynamic model. These classical formulations fall short in many important circumstances. For instance, smoothers obtained using quadratic penalties can fail when outliers are present in the data, and cannot track impulsive inputs and abrupt state changes. Motivated by these shortcomings, generalized Kalman smoothing formulations have been proposed in the last few years, replacing quadratic models with more suitable, often nonsmooth, convex functions. In contrast to classical models, these general estimators require use of iterated algorithms, and these have received increased attention from control, signal processing, machine learning, and optimization communities. In this survey we show that the optimization viewpoint provides the control and signal processing community great freedom in the development of novel modeling and inference frameworks for dynamical systems. We discuss general statistical models for dynamic systems, making full use of nonsmooth convex penalties and constraints, and providing links to important models in signal processing and machine learning. We also survey optimization techniques for these formulations, paying close attention to dynamic problem structure. Modeling concepts and algorithms are illustrated with numerical examples. (C) 2017 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2017. Vol. 86, 63-86 p.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-143068DOI: 10.1016/j.automatica.2017.08.011ISI: 000414115000008OAI: oai:DiVA.org:liu-143068DiVA: diva2:1159458
Note

Funding Agencies|National Science Foundation [DMS-1514559]; Washington Research Foundation Data Science Professorship; WRF Data Science Professorship; MIUR FIRB project [RBFR12M3AC]; Progetto di Ateneo [CPDA147754/14]; Linnaeus Center CADICS - Swedish Research Council [353-2012-5860]; ERC advanced grant LEARN [287381]; European Research Council

Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2017-11-22

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Ljung, Lennart
By organisation
Automatic ControlFaculty of Science & Engineering
In the same journal
Automatica
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 64 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf