Open Science Research Excellence
%0 Journal Article
%A Mohamed Boulouird and  Moha M'Rabet Hassani
%D 2007 
%J  International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 6, 2007
%T Blind Identification of MA Models Using Cumulants
%U http://waset.org/publications/6759
%V 6
%X In this paper, many techniques for blind identification of moving average (MA) process are presented. These methods utilize third- and fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed (i.i.d) non-Gaussian sequence that is not observed. Two nonlinear optimization algorithms, namely the Gradient Descent and the Gauss-Newton algorithms are exposed. An algorithm based on the joint-diagonalization of the fourth-order cumulant matrices (FOSI) is also considered, as well as an improved version of the classical C(q, 0, k) algorithm based on the choice of the Best 1-D Slice of fourth-order cumulants. To illustrate the effectiveness of our methods, various simulation examples are presented.

%P 869 - 873