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dc.contributor.authorHoteit, Ibrahim
dc.contributor.authorLuo, Xiaodong
dc.contributor.authorPham, Dinh-Tuan
dc.contributor.authorMoroz, Irene M.
dc.date.accessioned2015-05-14T06:45:48Z
dc.date.available2015-05-14T06:45:48Z
dc.date.issued2010-09-19
dc.identifier.citationParticle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters, AIP Conference Proceedings 1281 , 1075 (2010); doi: 10.1063/1.3497823
dc.identifier.doi10.1063/1.3497823
dc.identifier.urihttp://hdl.handle.net/10754/552770
dc.description.abstractOptimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture-based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz-96 model to illustrate the performance of the PKF.
dc.publisherAIP Publishing
dc.relation.urlhttp://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.3497823
dc.rightsArchived with thanks to AIP Conference Proceedings
dc.titleParticle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
dc.typeConference Paper
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Division
dc.conference.date2010-09-19 to 2010-09-25
dc.conference.nameInternational Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010
dc.conference.locationRhodes, GRC
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionThe Oxford-Man Institute, Eagle House, Walton Well Road, Oxford, 6ED, UK
dc.contributor.institutionCentre National de la Recherche Scientifique (CNRS), Grenoble, France
dc.contributor.institutionMathematical Institute, 24-29 St Giles’, Oxford, OX1 3LB, UK
kaust.personHoteit, Ibrahim
kaust.personLuo, Xiaodong
refterms.dateFOA2018-06-13T14:06:25Z


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