Estimation of linear parametric models using inverse filter criteria and higher order statistics

Author

Tugnait, Jitendra K.

Author_Institution

Dept. of Electr. Eng., Auburn Univ., AL, USA

Volume

41

Issue

11

fYear

1993

fDate

11/1/1993 12:00:00 AM

Firstpage

3196

Lastpage

3199

Abstract

Considers the problem of estimating the parameters of a stable, scalar ARMA (p, q) signal model (causal or noncausal, minimum phase or mixed phase) driven by an i.i.d. non-Gaussian sequence. The driving noise sequence is not observed. The Wiggins-Donoho (1978, 1991) class of inverse filter criteria for estimation of model parameters are analyzed and extended. These criteria have been considered in the past only for moving average inverse filters. These criteria are extended to general ARMA inverses. Computer simulation examples are presented to illustrate the proposed approaches

Keywords

filtering and prediction theory; parameter estimation; statistical analysis; IID nonGaussian sequence; computer simulation; higher order statistics; inverse filter criteria; linear parametric models; parameter estimation; scalar ARMA signal model; Computer simulation; Delay; Digital filters; Higher order statistics; IIR filters; Nonlinear filters; Parameter estimation; Parametric statistics; Signal processing; Speech processing;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.257255

Filename

257255