Two-dimensional autoregressive (2-D AR) model order estimation

Author

Aksasse, Brahim ; Radouane, Larbi

Author_Institution

Dept. of Phys., LESSI, Fez, Morocco

Volume

47

Issue

7

fYear

1999

fDate

7/1/1999 12:00:00 AM

Firstpage

2072

Lastpage

2077

Abstract

Much research has been devoted to the area of one-dimensional autoregressive (1-D AR) and autoregressive moving average (ARMA) model order selection. The most well-known solutions for this problem are the Akaike information criterion (AIC), MDL, and the minimum eigenvalue (MEV) criteria. On the other hand, all works in the 2-D case have focused on the problem of parameter estimation. In this correspondence, we extend the previous criteria to the 2-D AR model order determination. The model is assumed causal, stable, and spatially invariant with p₁×p₂ quarter-plane (QP) support. Numerical examples are given to illustrate the effectiveness of each method

Keywords

autoregressive processes; multidimensional signal processing; parameter estimation; signal processing; 2-D AR model order estimation; AIC; Akaike information criterion; MEV criteria; minimum eigenvalue criteria; spectral estimation; two-dimensional autoregressive model order estimation; Autoregressive processes; Covariance matrix; Eigenvalues and eigenfunctions; Estimation theory; Image restoration; Parameter estimation; Predictive models; Quadratic programming; Signal restoration; Two dimensional displays;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.771063

Filename

771063