AR model identification with unknown process order

Author

Katsikas, S.K. ; Likothanassis, S.D. ; Lainiotis, D.G.

Volume

38

Issue

5

fYear

1990

fDate

5/1/1990 12:00:00 AM

Firstpage

872

Lastpage

876

Abstract

A method for simultaneous autoregressive (AR) model order selection and identification is proposed, which is based on the adaptive Lainiotis filter (ALF). The method is not restricted to the Gaussian case, is applicable to online/adaptive operation, and is computationally efficient. It can be realized in a parallel processing fashion. The AR model order selection and identification problem is reformulated so that it can be fitted into the framework of a state space under uncertainty estimation problem framework. The ALF is briefly presented and its application to the specific problem is discussed. Simulation examples are presented to demonstrate the superior performance of the method in comparison with previously reported ones

Keywords

adaptive filters; filtering and prediction theory; parallel processing; signal processing; state-space methods; time series; AR model identification; adaptive Lainiotis filter; autoregressive model order selection; computationally efficient; online/adaptive operation; parallel processing; state space; uncertainty estimation problem; unknown process order; Computer science; Contracts; Frequency; Hardware; Harmonic analysis; Laboratories; Polynomials; Space technology; Speech; Symmetric matrices;

fLanguage

English

Journal_Title

Acoustics, Speech and Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

0096-3518

Type

jour

DOI

10.1109/29.56035

Filename

56035