By Topic

Order Estimation of Multivariate ARMA Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Cassar, T. ; Dept. of Syst. & Control Eng., Univ. of Malta, Msida, Malta ; Camilleri, K.P. ; Fabri, S.G.

Model order estimation is fundamental in the system identification process. In this paper, we generalize a previous multivariate autoregressive (AR) model order estimation method (J. Lardies and N. Larbi, ??A new method for model order selection and model parameter estimation in time domain,?? J. Sound Vibr., vol. 245, no. 2, 2001) to include multivariate autoregressive moving average (ARMA) models and propose a modified model order selection criterion. We discuss the performance analysis of the proposed criterion and show that it has a lower error probability for model order selection when compared to the criterion of G. Liang ??ARMA model order estimation based on the eigenvalues of the covariance matrix,??IEEE Trans. Signal Process., vol. 41, no. 10, pp. 3009-03009, Oct. 1993). A Monte-Carlo (MC) analysis of the model order selection performance under different noise variations and randomized model parameters is performed, allowing the MC results to be generalized across model parameter values and various noise levels. Finally we validate the model for both simulated data and real electroencephalographic (EEG) data by spectral fitting, using the model order selected by the proposed technique as compared to that selected by Akaike's Information Criterion (AIC). We demonstrate that with the proposed technique a better fit is obtained.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:4 ,  Issue: 3 )