Conditional moment generating functions for integrals and stochastic integrals

Author

Charalambous, Charalambos D. ; Elliott, Robert J. ; Krishnamurthy, Vikram

Author_Institution

Dept. of Electr. Eng., McGill Univ., Montreal, Que., Canada

Volume

4

fYear

1997

fDate

10-12 Dec 1997

Firstpage

3944

Abstract

We present two methods for computing filtered estimates for moments of integrals and stochastic integrals of continuous-time nonlinear systems. The first method utilizes recursive stochastic partial differential equations. The second method utilizes conditional moment generating functions. For the case of Gaussian systems the recursive computations involve integrations with respect to Gaussian densities, while the moment generating functions involve differentiations of parameter dependent ordinary stochastic differential equations. The second method is applied in the expectation maximization algorithm

Keywords

Kalman filters; continuous time systems; filtering theory; integral equations; matrix algebra; nonlinear control systems; partial differential equations; state estimation; stochastic processes; Gaussian densities; Gaussian systems; conditional moment generating functions; continuous-time nonlinear systems; expectation maximization algorithm; parameter dependent ordinary stochastic differential equations; recursive stochastic partial differential equations; stochastic integrals; Covariance matrix; Differential equations; Filtering theory; Integral equations; Nonlinear equations; Nonlinear systems; Stochastic processes; Stochastic systems; Testing; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.652479

Filename

652479