Multivariate ARMA modeling by scalar algorithms

Author

Chakraborty, Mrityunjoy ; Prasad, Surendra

Author_Institution

Dept. of Electr. Eng., Indian Inst. of Technol., New Delhi, India

Volume

41

Issue

4

fYear

1993

fDate

4/1/1993 12:00:00 AM

Firstpage

1692

Lastpage

1697

Abstract

An algorithm for multichannel autoregressive moving average (ARMA) modeling which uses scalar computations only and is well suited for parallel implementation is proposed. The given ARMA process is converted to an equivalent scalar, periodic ARMA process. The scalar autoregressive (AR) parameters are estimated by first deriving a set of modified Yule-Walker-type equations and then solving them by a parallel, order recursive algorithm. The moving average (MA) parameters are estimated by a least squares method from the estimates of the input samples obtained via a high-order, periodic AR approximation of the scalar process

Keywords

least squares approximations; parameter estimation; signal processing; statistical analysis; time series; ARMA process; Yule-Walker-type equations; least squares method; multichannel autoregressive moving average; multivariate ARMA modelling; parallel implementation; parameter estimation; periodic AR approximation; scalar algorithms; scalar autoregressive parameters; scalar computations; Acoustic noise; Chromium; Costs; Least squares methods; Maximum likelihood estimation; Parameter estimation; Resonance; Signal processing algorithms; Spectral analysis; Underwater acoustics;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.212746

Filename

212746