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Variational Iterations for Filtering and Smoothing with skew-t measurement noise
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Automatic Control)
Department of Automation Science and Engineering, Tampere University of Technology, Finland.ORCID iD: 0000-0003-0949-8848
Department of Automation Science and Engineering, Tampere University of Technology, Finland.ORCID iD: 0000-0003-1158-6951
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Automatic Control)
2015 (English)Report (Other academic)
Abstract [en]

In this technical report, some derivations for the filter and smoother proposed in [1] are presented. More specifically, the derivations for the cyclic iteration needed to solve the variational Bayes filter and smoother for state space models with skew t likelihood proposed in [1] are presented.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2015. , 9 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3083
Keyword [en]
skew t-distribution, skewness, t-distribution, robust filtering, Kalman filter, RTS smoother, variational Bayes
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-115741ISRN: LiTH-ISY-R-3083OAI: oai:DiVA.org:liu-115741DiVA: diva2:797463
Funder
Swedish Research Council, 621-2010-4301
Note

The technical report is related to the paper:

[1] H. Nurminen, T. Ardeshiri, R. Piché, and F. Gustafsson, “Robust inference for state-space models with skewed measurement noise,” submitted to Signal Processing Letters, 2015, [Online]. Available: http://arxiv.org/abs/1503.06606

Available from: 2015-03-24 Created: 2015-03-18 Last updated: 2015-04-07Bibliographically approved

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CiteExportLink to record
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