Identification of noncausal ARMA models of non-Gaussian processes using higher-order statistics

Author

Tugnait, Jitendra

Author_Institution

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

fYear

1990

fDate

3-6 Apr 1990

Firstpage

2387

Abstract

The problem of estimating the parameters of a stable, scalar, noncausal autoregressive moving average (ARMA) (p,q) signal model driven by an i.i.d. non-Gaussian sequence is considered. The driving noise sequence is not observed. Two methods are proposed and analyzed: one is a multistep linear method and the other is a nonlinear optimization method. Both methods exploit both the second- and third- (or higher-) order cumulants of the observed signal. The strong consistency of the two estimators is proved. The main focus is on the linear method. Extensions to include i.i.d. measurement noise (Gaussian or non-Gaussian) can be done easily

Keywords

parameter estimation; signal processing; statistical analysis; autoregressive moving average signal model; higher-order statistics; identification; independently identically distributed measurement noise; multistep linear method; nonGaussian processes; noncausal ARMA models; nonlinear optimization method; parameter estimation; second order cumulants; third order cumulants; Acoustic signal processing; Autoregressive processes; Chirp; Earth; Frequency; Gaussian noise; Higher order statistics; Noise measurement; Optimization methods; Parameter estimation; Parametric statistics; Signal design;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.116063

Filename

116063