File(s) under permanent embargo
Improved robust Kalman filtering for uncertain systems with missing measurements
chapter
posted on 2014-01-01, 00:00 authored by H Rezaei, Shady MohamedShady Mohamed, R M Esfanjani, Saeid NahavandiIn this paper, a novel robust finite-horizon Kalman filter is developed for discrete linear time-varying systems with missing measurements and normbounded parameter uncertainties. The missing measurements are modelled by a Bernoulli distributed sequence and the system parameter uncertainties are in the state and output matrices. A two stage recursive structure is considered for the Kalman filter and its parameters are determined guaranteeing that the covariances of the state estimation errorsare not more than the known upper bound. Finally, simulation results are presented to illustrate the outperformance of the proposed robust estimator compared with the previous results in the literature.
History
Title of book
Neural information processing : 21st International Conference ICONIP 2014 Kuching, Malaysia, November 3-6, 2014 Proceedings, Part IIIVolume
8836Series
Lecture notes in computer scienceChapter number
62Pagination
509 - 518Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319126425Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2014, SpringerExtent
83Editor/Contributor(s)
C Loo, K Yap, K Wong, A Teoh, K HuangUsage metrics
Categories
No categories selectedKeywords
Miss measurementNormbounded parameter uncertaintiesRobust Kalman filterState estimationScience & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Science, Information SystemsComputer Science, Theory & MethodsComputer Sciencenorm-bounded parameter uncertaintiesDISCRETE-TIME-SYSTEMSSTOCHASTIC-SYSTEMSBRAINDELAY
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC