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A Note on State Estimation as a Convex Optimization Problem
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2003 (English)Report (Other academic)
Abstract [en]

The Kalman filter computes the maximum a posteriori (MAP) estimate of the states for linear state space models with Gaussian noise. We interpret the Kalman filter as the solution to a convex optimization problem, and show that we can generalize the MAP state estimator to any noise with a log-concave density function and any combination of linear equality and convex inequality constraints on the states. We illustrate the principle on a hidden Markov model, where the state vector contains probabilities that are positive and sum to one.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2003. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2497
Keyword [en]
Kalman filter, Convex optimization, State estimation
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-55914ISRN: LiTH-ISY-R-2497OAI: oai:DiVA.org:liu-55914DiVA: diva2:316616
Available from: 2010-04-30 Created: 2010-04-30 Last updated: 2014-10-03Bibliographically approved

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Schön, ThomasGustafsson, Fredrik

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • 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
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