ARMA MODEL ORDER ESTIMATION OF NON-GAUSSIAN PROCESS USING THE DETERMINANT OF SUB-MATRICES OF A THIRD ORDER CUMULANTS COVARIANCE MATRIX

摘要

This article addresses the problem of model order estimation of an autoregressive moving average (ARMA) system from only third order cumulants of the output noisy observations of the system. The proposed technique looks for a corner in the tabulation of the cost function using the determinant of sub-matrices of a third order cumulants data covariance matrix derived from the observed data sequence. The observed sequence is excited by an unobservable input, and is corrupted by zero-mean Gaussian additive noise of unknown variance. The system is driven by a zero-mean independent and identically distributed (i.i.d.) non-Gaussian sequence. Simulation results are presented which demonstrate the performance of the proposed algorithm.