Nonlinear H-ARMA models

Author

Declercq, David ; Duvaut, Patrick

Author_Institution

CNRS, Cergy-Pontoise, France

Volume

4

fYear

1998

fDate

12-15 May 1998

Firstpage

2301

Abstract

We present some aspects of non-Gaussian H-ARMA models. After recalling that an H-ARMA process is obtained by passing an ARMA process through a Hermite polynomial nonlinearity, we describe the theoretical analysis of their cumulants and cumulant spectra. The main advantage of this kind of model is that the cumulant structure of the output can be deduced directly from the input covariance sequence. We give the analytic forms of these cumulants, together with some comments on their estimation. Then, we present the problems we are facing concerning the identification of the model´s parameters, and give a first (and naive) method for their estimation. We give some results obtained on synthetic data and finally conclude with some remarks on this class of processes

Keywords

autoregressive moving average processes; covariance analysis; filtering theory; higher order statistics; nonlinear filters; parameter estimation; polynomials; spectral analysis; ARMA filter; Hermite polynomial nonlinearity; cumulant spectra; cumulants; input covariance sequence; inversion method; model parameter identification; nonGaussian H-ARMA models; nonlinear H-ARMA models; nonlinear filter; statistical properties; synthetic data; Frequency; Gaussian noise; Nonlinear equations; Nonlinear filters; Polynomials;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on

Conference_Location

Seattle, WA

ISSN

1520-6149

Print_ISBN

0-7803-4428-6

Type

conf

DOI

10.1109/ICASSP.1998.681609

Filename

681609