Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters

Handle URI:
http://hdl.handle.net/10754/552770
Title:
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Authors:
Hoteit, Ibrahim ( 0000-0002-3751-4393 ) ; Luo, Xiaodong; Pham, Dinh-Tuan; Moroz, Irene M.
Abstract:
Optimal 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.
KAUST Department:
Physical Sciences and Engineering (PSE) Division
Citation:
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters, AIP Conference Proceedings 1281 , 1075 (2010); doi: 10.1063/1.3497823
Publisher:
AIP Publishing
Conference/Event name:
International Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010
Issue Date:
19-Sep-2010
DOI:
10.1063/1.3497823
Type:
Conference Paper
Additional Links:
http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.3497823
Appears in Collections:
Conference Papers; Physical Sciences and Engineering (PSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHoteit, Ibrahimen
dc.contributor.authorLuo, Xiaodongen
dc.contributor.authorPham, Dinh-Tuanen
dc.contributor.authorMoroz, Irene M.en
dc.date.accessioned2015-05-14T06:45:48Zen
dc.date.available2015-05-14T06:45:48Zen
dc.date.issued2010-09-19en
dc.identifier.citationParticle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters, AIP Conference Proceedings 1281 , 1075 (2010); doi: 10.1063/1.3497823en
dc.identifier.doi10.1063/1.3497823en
dc.identifier.urihttp://hdl.handle.net/10754/552770en
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.en
dc.publisherAIP Publishingen
dc.relation.urlhttp://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.3497823en
dc.rightsArchived with thanks to AIP Conference Proceedingsen
dc.titleParticle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filtersen
dc.typeConference Paperen
dc.contributor.departmentPhysical Sciences and Engineering (PSE) Divisionen
dc.conference.date2010-09-19 to 2010-09-25en
dc.conference.nameInternational Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010en
dc.conference.locationRhodes, GRCen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionThe Oxford‐Man Institute, Eagle House, Walton Well Road, Oxford, 6ED, UKen
dc.contributor.institutionCentre National de la Recherche Scientifique (CNRS), Grenoble, Franceen
dc.contributor.institutionMathematical Institute, 24‐29 St Giles’, Oxford, OX1 3LB, UKen
kaust.authorHoteit, Ibrahimen
kaust.authorLuo, Xiaodongen
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