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dc.contributor.authorGilitschenski, Igor*
dc.date.accessioned2021-02-11T11:14:41Z
dc.date.available2021-02-11T11:14:41Z
dc.date.issued2016*
dc.date.submitted2019-07-30 20:02:00*
dc.identifier35078*
dc.identifier.issn18673813*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/44863
dc.description.abstractThe goal of this work is improving existing and suggesting novel filtering algorithms for nonlinear dynamic state estimation. Nonlinearity is considered in two ways: First, propagation is improved by proposing novel methods for approximating continuous probability distributions by discrete distributions defined on the same continuous domain. Second, nonlinear underlying domains are considered by proposing novel filters that inherently take the underlying geometry of these domains into account.*
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.otherSensordatenfusion*
dc.subject.otherRichtungsstatistik*
dc.subject.otherDirectional Statistics*
dc.subject.otherStochastische Filterung*
dc.subject.otherSensor Data Fusion*
dc.subject.otherDichteapproximationStochastic Filtering*
dc.subject.otherDensity Approximation*
dc.titleDeterministic Sampling for Nonlinear Dynamic State Estimation*
dc.typebook
oapen.identifier.doi10.5445/KSP/1000051670*
oapen.relation.isPublishedBy68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2*
oapen.relation.isbn9783731504733*
oapen.pagesXVI, 167 p.*
oapen.volume18*


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