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Variational Iterations for Smoothing with Unknown Process and Measurement Noise Covariances
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. (Automatic Control)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. (Automatic Control)
2015 (English)Report (Other academic)
Abstract [en]

In this technical report, some derivations for the smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes smoother for linear state-space models with unknownprocess and measurement noise covariances in [1] are presented. Further, the variational iterations are compared with iterations of the Expectation Maximization (EM) algorithm for smoothing linear state-space models with unknown noise covariances.

[1] T. Ardeshiri, E. Özkan, U. Orguner, and F. Gustafsson, ApproximateBayesian smoothing with unknown process and measurement noise covariances, submitted to Signal Processing Letters, 2015.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. , 12 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3086
Keyword [en]
Adaptive smoothing, variational Bayes, sensor calibration, Rauch-Tung-Striebel smoother, Kalman filtering, noise covariance
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-120700ISRN: LiTH-ISY-R-3086OAI: oai:DiVA.org:liu-120700DiVA: diva2:849686
Available from: 2015-08-30 Created: 2015-08-21 Last updated: 2015-09-17Bibliographically approved

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Ardeshiri, TohidÖzkan, EmreOrguner, UmutGustafsson, Fredrik
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CiteExportLink to record
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