Inverse filter criteria for estimation of linear parametric models using higher order statistics

Author

Tugnait, Jitendra K.

Author_Institution

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

fYear

1991

fDate

14-17 Apr 1991

Firstpage

3101

Abstract

The author considers the problem of estimating the parameters of a stable, scalar ARMA (autoregressive moving average) signal model (causal or noncausal, minimum phase or mixed phase) driven by an independent and identically distributed nonGaussian sequence. The driving noise sequence is not observed. The Wiggins-Donoho class of inverse filter criteria for estimation of model parameters are analyzed and extended to general ARMA inverses. A class of criteria for consistent parameter estimation in colored Gaussian noise is proposed and analyzed

Keywords

filtering and prediction theory; parameter estimation; signal processing; statistical analysis; ARMA inverses; Wiggins-Donoho class; autoregressive moving average; causal signal; colored Gaussian noise; higher order statistics; identically distributed nonGaussian sequence; independent sequence; inverse filter criteria; linear parametric models; minimum phase; mixed phase; noncausal signal; parameter estimation; scalar ARMA; signal processing; Gaussian noise; Higher order statistics; Inverse problems; Noise measurement; Nonlinear filters; Parameter estimation; Parametric statistics; Phase estimation; Statistical analysis; Yield estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on

Conference_Location

Toronto, Ont.

ISSN

1520-6149

Print_ISBN

0-7803-0003-3

Type

conf

DOI

10.1109/ICASSP.1991.150111

Filename

150111