DocumentCode
637562
Title
Sufficiently informative measurements for stability of approximate conditional mean estimators
Author
Techakesari, Onvaree ; Ford, Jason J.
Author_Institution
Sci. & Eng. Fac., Queensland Univ. of Technologly, Brisbane, QLD, Australia
fYear
2012
fDate
15-16 Nov. 2012
Firstpage
241
Lastpage
246
Abstract
This paper establishes sufficient conditions to bound the error in perturbed conditional mean estimates derived from a perturbed model (only the scalar case is shown in this paper but a similar result is expected to hold for the vector case). The results established here extend recent stability results on approximating information state filter recursions to stability results on the approximate conditional mean estimates. The presented filter stability results provide bounds for a wide variety of model error situations.
Keywords
filtering theory; approximate conditional mean estimators; filter stability results; information state filter recursions; informative measurements; model error situations; perturbed conditional mean estimates; perturbed model; Approximation methods; Asymptotic stability; Computational modeling; Hidden Markov models; Noise; Stability analysis; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Control Conference (AUCC), 2012 2nd Australian
Conference_Location
Sydney, NSW
Print_ISBN
978-1-922107-63-3
Type
conf
Filename
6613203
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