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Extended Kalman Filter Modifications Based on an Optimization View Point
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.ORCID iD: 0000-0002-1971-4295
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6957-2603
2015 (English)In: 18th International Conference of Information Fusion, Institute of Electrical and Electronics Engineers (IEEE), 2015Conference paper (Refereed)
Abstract [en]

The extended Kalman filter (EKF) has been animportant tool for state estimation of nonlinear systems sinceits introduction. However, the EKF does not possess the same optimality properties as the Kalman filter, and may perform poorly. By viewing the EKF as an optimization problem it is possible to, in many cases, improve its performance and robustness. The paper derives three variations of the EKF by applying different optimisation algorithms to the EKF costfunction and relate these to the iterated EKF. The derived filters are evaluated in two simulation studies which exemplify the presented filters.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2015.
Keyword [en]
extended Kalman filter, optimization, iterated extended Kalman filter
National Category
Control Engineering Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-120383ISBN: 978-098244386-6 (print)OAI: oai:DiVA.org:liu-120383DiVA: diva2:844060
Conference
18th International Conference of Information Fusion, Washington, D.C., USA, July 6-9, 2015
Projects
Scalable Kalman Filters
Funder
VINNOVA, LINK-SICSwedish Research CouncilSecurity Link
Available from: 2015-08-03 Created: 2015-08-03 Last updated: 2016-08-31

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