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The Ensemble Kalman Filter and its Relations to Other Nonlinear Filters
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering. Swedish Defence Research Agency (FOI), Linköping, Sweden.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2015 (English)In: Proceedings of the 2015 European Signal Processing Conference (EUSIPCO 2015), Institute of Electrical and Electronics Engineers (IEEE), 2015, 1236-1240 p.Conference paper, Published paper (Refereed)
Abstract [en]

The Ensemble Kalman filter (EnKF) is a standard algorithm in oceanography and meteorology, where it has got thousands of citations. It is in these communities appreciated since it scales much better with state dimension n than the standard Kalman filter (KF). In short, the EnKF propagates ensembles with N state realizations instead of mean values and covariance matrices and thereby avoids the computational and storage burden of working on n×n matrices. Perhaps surprising, very little attention has been devoted to the EnKF in the signal processing community. In an attempt to change this, we present the EnKF in a Kalman filtering context. Furthermore, its application to nonlinear problems is compared to sigma point Kalman filters and the particle filter, so as to reveal new insights and improvements for high-dimensional filtering algorithms in general. A simulation example shows the EnKF performance in a space debris tracking application.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015. 1236-1240 p.
Series
European Signal Processing Conference, ISSN 2076-1465
Keyword [en]
Kalman filter; ensemble Kalman filter; sigma point Kalman filter; UKF; particle filter
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-121079DOI: 10.1109/EUSIPCO.2015.7362581ISI: 000377943800249ISBN: 978-0-9928626-3-3 (print)OAI: oai:DiVA.org:liu-121079DiVA: diva2:851508
Conference
23rd European Signal Processing Conference,Nice, France, Aug 31-Sept 4, 2015
Projects
Scalable Kalman Filters
Funder
Swedish Research Council
Available from: 2015-09-05 Created: 2015-09-05 Last updated: 2016-07-29Bibliographically approved

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Roth, MichaelFritsche, CarstenHendeby, GustafGustafsson, Fredrik
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