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Nonlinear state and parameter estimation of spatially distributed systems

dc.contributor.authorSawo, Felix*
dc.date.accessioned2021-02-11T21:08:04Z
dc.date.available2021-02-11T21:08:04Z
dc.date.issued2009*
dc.date.submitted2019-07-30 20:01:58*
dc.identifier34479*
dc.identifier.issn18673813*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/54761
dc.description.abstractIn this thesis two probabilistic model-based estimators are introduced that allow the reconstruction and identification of space-time continuous physical systems. The Sliced Gaussian Mixture Filter (SGMF) exploits linear substructures in mixed linear/nonlinear systems, and thus is well-suited for identifying various model parameters. The Covariance Bounds Filter (CBF) allows the efficient estimation of widely distributed systems in a decentralized fashion.*
dc.languageEnglish*
dc.relation.ispartofseriesKarlsruhe Series on Intelligent Sensor-Actuator-Systems, Universität Karlsruhe / Intelligent Sensor-Actuator-Systems Laboratory*
dc.subjectQA75.5-76.95*
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer scienceen_US
dc.subject.othersensor network*
dc.subject.othernonlinear estimation*
dc.subject.otherdistributed-parameter system*
dc.titleNonlinear state and parameter estimation of spatially distributed systems*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000011485*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783866443709*
oapen.pagesXI, 153 p.*
oapen.volume5*
peerreview.review.typeFull text
peerreview.anonymityAll identities known
peerreview.reviewer.typeEditorial board member
peerreview.reviewer.typeExternal peer reviewer
peerreview.review.stagePre-publication
peerreview.open.reviewNo
peerreview.publish.responsibilityBooks or series editor
peerreview.id51a542ec-eaeb-47c2-861d-6022e981a97a
peerreview.titleDissertations in Series (Dissertationen in Schriftenreihe)


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