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State Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties

dc.contributor.authorNoack, Benjamin*
dc.date.accessioned2021-02-12T04:28:40Z
dc.date.available2021-02-12T04:28:40Z
dc.date.issued2014*
dc.date.submitted2019-07-30 20:02:00*
dc.identifier35025*
dc.identifier.issn18673813*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/59976
dc.description.abstractState estimation techniques for centralized, distributed, and decentralized systems are studied. An easy-to-implement state estimation concept is introduced that generalizes and combines basic principles of Kalman filter theory and ellipsoidal calculus. By means of this method, stochastic and set-membership uncertainties can be taken into consideration simultaneously. Different solutions for implementing these estimation algorithms in distributed networked systems are presented.*
dc.languageEnglish*
dc.relation.ispartofseriesKarlsruhe Series on Intelligent Sensor-Actuator-Systems / Karlsruher Institut für Technologie, 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.otherdistributed estimation*
dc.subject.otherKalman filter*
dc.subject.otherset-membership estimation*
dc.subject.otherBayesian state estimation*
dc.titleState Estimation for Distributed Systems with Stochastic and Set-membership Uncertainties*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000036878*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731501244*
oapen.pagesXVIII, 257 p.*
oapen.volume14*
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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