Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/30137
Title: | Combined cubature Kalman and smooth variable structure filtering: A robust nonlinear estimation strategy |
Authors: | Gadsden SA Al-Shabi M Arasaratnam I Habibi SR |
Department: | Mechanical Engineering |
Keywords: | 40 Engineering;4001 Aerospace Engineering |
Publication Date: | Mar-2014 |
Publisher: | Elsevier |
Abstract: | In this paper, nonlinear state estimation problems with modeling uncertainties are considered. As demonstrated recently in literature, the cubature Kalman filter (CKF) provides the closest known approximation to the Bayesian filter in the sense of preserving second-order information contained in noisy measurements under the Gaussian assumption. The smooth variable structure filter (SVSF) has also been recently introduced and has been shown to be robust to modeling uncertainties. In an effort to utilize the accuracy of the CKF and the robustness of the SVSF, the CKF and SVSF have been combined resulting in an algorithm referred to as the CK-SVSF. The robustness and accuracy of the CK-SVSF was validated by testing it on two different computer problems, namely, a target tracking problem and the estimation of the effective bulk modulus in an electrohydrostatic actuator. © 2013 Elsevier B.V. |
metadata.dc.rights.license: | Attribution-NonCommercial-NoDerivs - CC BY-NC-ND |
URI: | http://hdl.handle.net/11375/30137 |
metadata.dc.identifier.doi: | https://doi.org/10.1016/j.sigpro.2013.08.015 |
ISSN: | 1872-7557 1872-7557 |
Appears in Collections: | Mechanical Engineering Publications |
Files in This Item:
File | Description | Size | Format | |
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008-1-s2.0-S0165168413003253-main.pdf | 1.17 MB | Adobe PDF | View/Open |
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