Estimation in generalized uncertain-stochastic linear regression

Author

Borisov, Andrei V.

Author_Institution

Dept. of Appl. Math., Moscow Aviation Inst., Russia

fYear

1992

fDate

1992

Firstpage

1222

Abstract

The author considers the problem of optimally estimating a certain finite-dimensional vector, which is the result of a certain linear transformation of processes from a special class of stochastic and uncertain processes. Optimality of the estimate implies minimization of a minimax-stochastic criterion. A linear model of observations which contains random disturbances and has a discrete-continuous structure is assumed. The optimal estimate must be found as a linear operator of observations. Necessary and sufficient conditions for the linear estimate optimality are presented. The optimal filtering algorithm for uncertain-stochastic differential systems is obtained as an application of this estimation theory

Keywords

estimation theory; filtering and prediction theory; stochastic processes; discrete-continuous structure; finite-dimensional vector; generalized uncertain-stochastic linear regression; linear model; linear transformation; minimax-stochastic criterion; optimal filtering algorithm; random disturbances; uncertain processes; Covariance matrix; Estimation theory; Extraterrestrial measurements; Filtering algorithms; Linear regression; Mathematics; Stochastic processes; Sufficient conditions; Symmetric matrices; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1992., Proceedings of the 31st IEEE Conference on

Conference_Location

Tucson, AZ

Print_ISBN

0-7803-0872-7

Type

conf

DOI

10.1109/CDC.1992.371523

Filename

371523