The Ensemble Kalman Filter and its Relations to Other Nonlinear Filters
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 (Refereed)
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.
, European Signal Processing Conference, ISSN 2076-1465
Kalman filter; ensemble Kalman filter; sigma point Kalman filter; UKF; particle filter
Control Engineering Signal Processing
IdentifiersURN: urn:nbn:se:liu:diva-121079DOI: 10.1109/EUSIPCO.2015.7362581ISI: 000377943800249ISBN: 978-0-9928626-3-3OAI: oai:DiVA.org:liu-121079DiVA: diva2:851508
23rd European Signal Processing Conference,Nice, France, Aug 31-Sept 4, 2015
ProjectsScalable Kalman Filters
FunderSwedish Research Council