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ML Estimation of Process Noise Variance in Dynamic Systems
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2010 (English)Report (Other academic)
Abstract [en]

The performance of a non-linear filter hinges in the end on the accuracy of the assumed non-linear model of the process. In particular, the process noise covariance $Q$ is hard to get by physical modeling and dedicated system identification experiments. We propose a variant of the expectation maximization (EM) algorithm which iteratively estimates the unobserved state sequence and $Q$ based on the observations of the process. The extended Kalman smoother (EKS) is the instrument to find the unobserved state sequence. Our contribution fills a gap in literature, where previously only the linear Kalman smoother and particle smoother have been applied. The algorithm will be important for future industrial robots with more flexible structures, where the particle smoother cannot be applied due to the high state dimension. The proposed method is compared to two alternative methods on a simulated robot.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2010. , 6 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2969
Keyword [en]
Robotic manipulators, Extended Kalman filters, Smoothing filters, Identification, Maximum likelihood, Covariance matrices
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-88978ISRN: LiTH-ISY-R-2969OAI: oai:DiVA.org:liu-88978DiVA: diva2:606586
Projects
Vinnova Excellence Center LINK-SIC
Available from: 2013-02-19 Created: 2013-02-19 Last updated: 2014-06-17Bibliographically approved

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ML Estimation of Process Noise Variance in Dynamic Systems(633 kB)179 downloads
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Axelsson, PatrikOrguner, UmutGustafsson, FredrikNorrlöf, Mikael

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