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A Note on the Particle Filter with Posterior Gaussian Resampling
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Electricity.
2011 (English)In: Tellus. Series A, Dynamic meteorology and oceanography, ISSN 0280-6495, E-ISSN 1600-0870, Vol. 58, no 4Article in journal (Refereed) Published
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Text
Abstract [en]

Particle filter (PF) is a fully non-linear filter with Bayesian conditional probability estimation, compared here with the well-known ensemble Kalman filter (EnKF). A Gaussian resampling (GR) method is proposed to generate the posterior analysis ensemble in an effective and efficient way. The Lorenz model is used to test the proposed method. The PF with Gaussian resampling (PFGR) can approximate more accurately the Bayesian analysis. The present work demonstrates that the proposed PFGR possesses good stability and accuracy and is potentially applicable to large-scale data assimilation problems.

Place, publisher, year, edition, pages
2011. Vol. 58, no 4
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
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URN: urn:nbn:se:uu:diva-268982OAI: oai:DiVA.org:uu-268982DiVA: diva2:899737
Available from: 2016-02-02 Created: 2015-12-11 Last updated: 2017-11-30

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http://www.tellusa.net/index.php/tellusa/article/view/14798

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Uzunoglu, Bahri

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