AR, ARMA, and AR-in-noise modeling by fitting windowed correlation data

Author

Jackson, Leland B. ; Huang, Jianguo ; Richards, Kevin P. ; Chen, Haiguang

Author_Institution

Dept. of Electr. Eng., Rhode Island Univ., Kingston, RI, USA

Volume

37

Issue

10

fYear

1989

fDate

10/1/1989 12:00:00 AM

Firstpage

1608

Lastpage

1612

Abstract

A method for autoregressive (AR) modeling of stationary stochastic signals is extended to AR moving-average (ARMA) models, including the special case of AR signals in white noise. Both AR and ARMA examples are presented. The method differs from the well-known method of overdetermined normal equations in that fitting error, not equation error, is minimized, and significantly improved performance is obtained. Iterative algorithms patterned after the Steiglitz-McBride (1965) deterministic method are derived to solve the resulting nonlinear equations

Keywords

correlation theory; iterative methods; signal processing; white noise; AR modelling; AR moving-average; AR signals; AR-in-noise modeling; ARMA; deterministic method; iterative algorithms; nonlinear equations; stationary stochastic signals; white noise; windowed correlation data; Acoustic signal processing; Autocorrelation; Equations; Filtering; Filters; Iterative algorithms; Predictive models; Stochastic processes; White noise; Writing;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.35404

Filename

35404