Approximate series representations of second-order stochastic processes: applications to signal detection and estimation

Author

Navarro-Moreno, Jessú ; Ruiz-Molina, Juan Carlos ; Fernández-Alcalá, Rosa M.

Author_Institution

Dept. of Stat. & Operations Res., Univ. of Jaen, Spain

Volume

49

Issue

6

fYear

2003

fDate

6/1/2003 12:00:00 AM

Firstpage

1574

Lastpage

1578

Abstract

Two distinct approximate series representations are obtained for the entire class of measurable, second-order stochastic processes defined on any interval of the real line. They include as particular cases all earlier approximate representations based on the Rayleigh-Ritz method. It is also shown that each of them converges with a different type of convergence. Finally, two applications in statistical communication theory are presented.

Keywords

approximation theory; convergence of numerical methods; information theory; parameter estimation; series (mathematics); signal detection; signal representation; stochastic processes; Rayleigh-Ritz method; approximate series representations; convergence; second-order stochastic process; signal detection; signal estimation; statistical communication theory; Convergence; Eigenvalues and eigenfunctions; Integral equations; Kernel; Operations research; Signal detection; Signal processing; Statistics; Stochastic processes; White noise;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/TIT.2003.811918

Filename

1201083