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
Link To Document :
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