Title :
On estimating ARMA model orders
Author :
Al-Smadi, Adnan ; Wilkes, D. Mitchell
Author_Institution :
Dept. of Ind. Technol., Tennessee State Univ., Nashville, TN, USA
Abstract :
In system identification where a given sequence represents the output of an autoregressive moving-average (ARMA) process, the estimation of the proper ARMA model order and parameters is an important problem. In this paper, we propose a method for estimating the orders of an ARMA process from the observations of the noise-corrupted output using third order cumulants. The observed sequence is modeled as the output of an ARMA system that is excited by an unobservable input, and is corrupted by white, zero-mean additive Gaussian noise. This method is based on the minimum eigenvalue of a covariance matrix derived from the observed data sequence. This is a generalization of the approach of Liang et al. [1,2], which eliminates the estimation of the ai and bi coefficients
Keywords :
Gaussian noise; autoregressive moving average processes; covariance matrices; eigenvalues and eigenfunctions; identification; white noise; ARMA model orders; covariance matrix; minimum eigenvalue; noise-corrupted output; observed data sequence; system identification; third order cumulants; white noise; zero-mean additive Gaussian noise; Additive noise; Computer industry; Covariance matrix; Eigenvalues and eigenfunctions; Equations; Frequency estimation; Gaussian noise; Predictive models; Signal processing algorithms; System identification;
Conference_Titel :
Circuits and Systems, 1996. ISCAS '96., Connecting the World., 1996 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
0-7803-3073-0
DOI :
10.1109/ISCAS.1996.541757
