Fractional central difference Kalman filter with unknown prior information

Liu, T; Cheng, S; Wei, Y; Li, A; Wang, Y

Fractional central difference Kalman filter with unknown prior information

Liu, T

Cheng, S Wei, Y Li, A Wang, Y

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Publication Type:: Journal Article
Citation:: Signal Processing, 2019, 154 pp. 294 - 303
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Liu, T https://orcid.org/0000-0001-8107-8587	en_US
dc.contributor.author	Cheng, S	en_US
dc.contributor.author	Wei, Y	en_US
dc.contributor.author	Li, A	en_US
dc.contributor.author	Wang, Y	en_US
dc.date.accessioned	2020-03-27T03:10:04Z
dc.date.available	2021-01-01T18:06:02Z
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Signal Processing, 2019, 154 pp. 294 - 303	en_US
dc.identifier.issn	0165-1684	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139564
dc.description.abstract	© 2018 In this paper, a generalized fractional central difference Kalman filter for nonlinear discrete fractional dynamic systems is proposed. Based on the Stirling interpolation formula, the presented algorithm can be implemented as no derivatives are needed. Besides, in order to estimate the state with unknown prior information, a maximum a posteriori principle based adaptive fractional central difference Kalman filter is derived. The adaptive algorithm can estimate the noise statistics and system state simultaneously. The unbiasedness of the proposed algorithm is analyzed. Several numerical examples demonstrate the accuracy and effectiveness of the two Kalman filters.	en_US
dc.relation.ispartof	Signal Processing	en_US
dc.relation.isbasedon	10.1016/j.sigpro.2018.08.006	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Networking & Telecommunications	en_US
dc.title	Fractional central difference Kalman filter with unknown prior information	en_US
dc.type	Journal Article
utslib.citation.volume	154	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	154	en_US

Abstract:

© 2018 In this paper, a generalized fractional central difference Kalman filter for nonlinear discrete fractional dynamic systems is proposed. Based on the Stirling interpolation formula, the presented algorithm can be implemented as no derivatives are needed. Besides, in order to estimate the state with unknown prior information, a maximum a posteriori principle based adaptive fractional central difference Kalman filter is derived. The adaptive algorithm can estimate the noise statistics and system state simultaneously. The unbiasedness of the proposed algorithm is analyzed. Several numerical examples demonstrate the accuracy and effectiveness of the two Kalman filters.

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http://hdl.handle.net/10453/139564