Authors:
M. Boulouird
1
;
M. M. Hassani
1
and
G. Favier
2
Affiliations:
1
Faculty of Sciences Semlalia, Morocco
;
2
Laboratoire d’Informatique, Signaux et Systèmes de Sophia-Antipolis I3S (CNRS/UNSA), France
Keyword(s):
MA system identification, Higher-Order Statistics, Estimation parameter, Linear algebra solution, Gradient descent algorithm, Gauss-Newton algorithm, Cumulants.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
Abstract:
In this paper nonlinear optimization algorithms, namely the Gradient descent and the Gauss-Newton algorithms, are proposed for blind identification of MA models. A relationship between third and fourth order cumulants of the noisy system output and the MA parameters is exploited to build a set of nonlinear equations that is solved by means of the two nonlinear optimization algorithms above cited. Simulation results are presented to compare the performance of the proposed algorithms.