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    Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters

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    Type
    Conference Paper
    Authors
    Hoteit, Ibrahim cc
    Luo, Xiaodong
    Pham, Dinh-Tuan
    Moroz, Irene M.
    KAUST Department
    Earth Fluid Modeling and Prediction Group
    Earth Science and Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2010-09-20
    Online Publication Date
    2010-09-20
    Print Publication Date
    2010
    Permanent link to this record
    http://hdl.handle.net/10754/552770
    
    Metadata
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    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.
    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
    DOI
    10.1063/1.3497823
    Additional Links
    http://scitation.aip.org/content/aip/proceeding/aipcp/10.1063/1.3497823
    ae974a485f413a2113503eed53cd6c53
    10.1063/1.3497823
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Earth Science and Engineering Program

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