A statistical foundation for the use of the conjugate gradient method in deconvolution

Author

Elias, Eric D. ; Pattipati, Krishna R.

Author_Institution

Dept. of Electr. & Syst. Eng., Connecticut Univ., Storrs, CT, USA

Volume

3

fYear

1996

fDate

11-13 Dec 1996

Firstpage

3131

Abstract

A number of papers have suggested the use of conjugate gradient algorithms for deconvolution applications. The algebraic properties of conjugate gradient algorithms are well known with respect to minimization and linear equation solutions. Deconvolution is a subject rich in theory pertaining to linear systems and stochastic processes. This paper presents a theoretical explanation of the conjugate gradient deconvolution method in terms of signal statistics and parallel linear filters

Keywords

FIR filters; Wiener filters; conjugate gradient methods; deconvolution; filtering theory; identification; linear systems; signal sampling; statistics; conjugate gradient method; deconvolution; linear systems; parallel linear filters; signal statistics; statistical foundation; stochastic processes; Deconvolution; Equations; Finite impulse response filter; Gradient methods; Least squares approximation; Least squares methods; Linear systems; Signal processing; Signal processing algorithms; Statistics;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1996., Proceedings of the 35th IEEE Conference on

Conference_Location

Kobe

ISSN

0191-2216

Print_ISBN

0-7803-3590-2

Type

conf

DOI

10.1109/CDC.1996.573609

Filename

573609