ARMA spectral estimation of narrow-band processes via model reduction

Author

Wahlberg, Bo

Author_Institution

Dept. of Electr. Eng., Linkoping Univ., Sweden

Volume

38

Issue

7

fYear

1990

fDate

7/1/1990 12:00:00 AM

Firstpage

1144

Lastpage

1154

Abstract

The problem of estimating autoregressive moving average ARMA models for narrowband processes is considered. The following approach is proposed. Estimate a high-order autoregressive (AR) approximation of the process. By model reduction, based on a truncated internally balanced realization or optimal Hankel-norm model reduction, reduce the order of this high-order AR estimate to find a lower-order ARMA model. This algorithm gives ARMA spectral estimates with excellent resolution properties, without using iterative numerical minimization methods as for the maximum-likelihood method. How to take the narrowband assumption into account in the model reduction step is discussed in detail

Keywords

parameter estimation; spectral analysis; ARMA model; autoregressive moving average; model reduction; narrowband processes; optimal Hankel-norm model reduction; spectral estimation; truncated internally balanced realization; Autoregressive processes; Iterative algorithms; Iterative methods; Maximum likelihood estimation; Minimization methods; Narrowband; Parameter estimation; Reduced order systems; Speech analysis; Technological innovation;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.57543

Filename

57543