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Smoothed State Estimation via Efficient Solution of Linear Equations
Tianjin Univ Technol & Educ, Sch Automat & Elect Engn, Tianjin Key Lab Informat Sensing & Intelligent Co, Tianjin, Peoples R China.
Univ Newcastle, Fac Engn & Built Environm, Callaghan, NSW, Australia.
Univ Newcastle, Fac Engn & Built Environm, Callaghan, NSW, Australia.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.ORCID iD: 0000-0001-5183-234X
2017 (English)In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 1613-1618Article in journal (Refereed) Published
Abstract [en]

This paper addresses the problem of computing fixed interval smoothed state estimates of a linear time varying Gaussian stochastic system. There already exist many algorithms that perform this computation, but all of them impose certain restrictions on system matrices in order for them to be applicable. This paper develops a new forwards–backwards pass algorithm that is applicable under the mildest restrictions possible - namely that the smoothed state distribtions exists in forms that can be characterised by means and covariances, for which this paper also develops a new necessary and sufficient condition.

Place, publisher, year, edition, pages
2017. Vol. 50, no 1, p. 1613-1618
Keywords [en]
Filtering and smoothing, Estimation and filtering, Stochastic system identification, Bayesian methods
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-354389DOI: 10.1016/j.ifacol.2017.08.323ISI: 000423845200262OAI: oai:DiVA.org:uu-354389DiVA, id: diva2:1229128
Conference
20th World Congress of the International-Federation-of-Automatic-Control (IFAC), JUL 09-14, 2017, Toulouse, FRANCE
Funder
Australian Research Council, DP140104350Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-06-29Bibliographically approved

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Schön, Thomas B.

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