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Optimal Estimation With Missing Observations via Balanced Time-Symmetric Stochastic Models
KTH, School of Engineering Sciences (SCI), Centres, Center for Industrial and Applied Mathematics, CIAM. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre. Shanghai Jiao Tong Univ, Peoples R China.ORCID iD: 0000-0002-2681-8383
2017 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, no 11, p. 5590-5603Article in journal (Refereed) Published
Abstract [en]

We consider data fusion for the purpose of smoothing and interpolation based on observation records with missing data. Stochastic processes are generated by linear stochastic models. The paper begins by drawing a connection between time reversal in stochastic systems and all-pass extensions. A particular normalization (choice of basis) between the two time-directions allows the two to share the same orthonormalized state process and simplifies the mathematics of data fusion. In this framework, we derive symmetric and balanced Mayne-Fraser-like formulas that apply simultaneously to continuous-time smoothing and interpolation, providing a definitive unification of these concepts. The absence of data over subintervals requires in general a hybrid filtering approach involving both continuous-time and discrete-time filtering steps.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 62, no 11, p. 5590-5603
Keywords [en]
Filtering theory, Kalman filters, missing observations
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-217419DOI: 10.1109/TAC.2017.2689685ISI: 000413837700008Scopus ID: 2-s2.0-85036458551OAI: oai:DiVA.org:kth-217419DiVA, id: diva2:1158182
Note

QC 20171117

Available from: 2017-11-17 Created: 2017-11-17 Last updated: 2017-12-15Bibliographically approved

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Lindquist, Anders

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