Optimal deconvolution based on polynomial methods

Author

Ahlén, Anders ; Sternad, Mikael

Author_Institution

Dept. of Technol., Uppsala Univ., Sweden

Volume

37

Issue

2

fYear

1989

fDate

2/1/1989 12:00:00 AM

Firstpage

217

Lastpage

226

Abstract

The problem of estimating the input to a known linear system is treated in a shift operator polynomial formulation. The mean-square estimation error is minimized. The input and a colored measurement noise are described by independent ARMA (autoregressive moving average) processes. The filter is calculated by performing a spectral factorization and solving a polynomial equation. The approach can be applied to input prediction, filtering, and smoothing problems as well as to the use of prefilters in the quadratic criterion. It applies to nonminimum-phase as well as unstable systems, as illustrated by two examples

Keywords

estimation theory; filtering and prediction theory; linear systems; polynomials; filter; independent ARMA; input estimation; input prediction; known linear system; mean-square estimation error; nonminimum-phase; optimal deconvolution; polynomial methods; prefilters; quadratic criterion; shift operator polynomial formulation; smoothing; spectral factorization; unstable systems; Colored noise; Deconvolution; Equations; Estimation error; Filtering; Filters; Linear systems; Noise measurement; Polynomials; Smoothing methods;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.21684

Filename

21684