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Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/24119
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dc.contributor.authorElliott, R.-
dc.contributor.authorVan Der Hoek, J.-
dc.date.issued2006-
dc.identifier.citationIEEE Transactions on Automatic Control, 2006; 51(4):686-689-
dc.identifier.issn0018-9286-
dc.identifier.urihttp://hdl.handle.net/2440/24119-
dc.description©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.description.abstractOptimal mean square linear estimators are determined for general uncorrelated noise. We allow the noise variance matrix in the observation process to be singular. This requires properties of generalized inverses which are developed in Section II. The proofs appear to be new. When there are two observation sequences the optimal method of recursively fusing the two is determined. We derive a new formula for the covariance of the two estimates which then provides exact dynamics for a fused estimate.-
dc.description.statementofresponsibilityElliott, R.J. ; van der Hoek, J.-
dc.language.isoen-
dc.publisherIEEE-Inst Electrical Electronics Engineers Inc-
dc.source.urihttp://dx.doi.org/10.1109/tac.2006.872768-
dc.subjectdata fusion-
dc.subjectoptimal linear estimation-
dc.titleOptimal linear estimation and data fusion-
dc.typeJournal article-
dc.identifier.doi10.1109/TAC.2006.872768-
pubs.publication-statusPublished-
Appears in Collections:Applied Mathematics publications
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