DocumentCode
2977382
Title
A convergence condition for optimal nonlinear filtering for systems with unknown parameters
Author
Casiello, Francisco A. ; Loparo, Kenneth A.
Author_Institution
Dept. of Syst. Eng., Case Western Reserve Univ., Cleveland, OH, USA
fYear
1988
fDate
7-9 Dec 1988
Firstpage
1984
Abstract
An examination is made of the problem of estimating the state of linear stochastic plant with unknown parameters taking values in a finite set. Stochastic stability theory is used to establish conditions under which the a posteriori probabilities defined on a finite parameter set converge almost surely, both in continuous and discrete time. An example of what happens if the conditions are not satisfied is given
Keywords
convergence; filtering and prediction theory; optimisation; state estimation; stochastic systems; a posteriori probabilities; continuous-time systems; convergence condition; discrete-time systems; linear system; optimal nonlinear filtering; stability; state estimation; stochastic system; unknown parameters; Convergence; Covariance matrix; Differential equations; Filtering; Probability distribution; Stability; State estimation; Stochastic processes; Stochastic systems; Systems engineering and theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1988., Proceedings of the 27th IEEE Conference on
Conference_Location
Austin, TX
Type
conf
DOI
10.1109/CDC.1988.194680
Filename
194680
Link To Document