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Optimal Estimation With Missing Observations via Balanced Time-Symmetric Stochastic Models
KTH, Skolan för teknikvetenskap (SCI), Centra, Strategiskt centrum för industriell och tillämpad matematik, CIAM. KTH, Skolan för elektro- och systemteknik (EES), Centra, ACCESS Linnaeus Centre. Shanghai Jiao Tong Univ, Peoples R China.ORCID-id: 0000-0002-2681-8383
2017 (engelsk)Inngår i: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 62, nr 11, s. 5590-5603Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE), 2017. Vol. 62, nr 11, s. 5590-5603
Emneord [en]
Filtering theory, Kalman filters, missing observations
HSV kategori
Identifikatorer
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
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QC 20171117

Tilgjengelig fra: 2017-11-17 Laget: 2017-11-17 Sist oppdatert: 2017-12-15bibliografisk kontrollert

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