Title :
Orthogonalization of correlated Gaussian signals for Volterra system identification
Author_Institution :
Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA
Abstract :
This article presents a simple method for orthogonalizing correlated Gaussian input signals for identification of truncated Volterra systems of arbitrary order of nonlinearity P and memory length N. The procedure requires a Gram-Schmidt orthogonalizer for a vector containing N elements and some nonlinear processing of the output elements of the Gram-Schmidt processor. However, the nonlinear processors do not depend on the statistics of the input signals and, consequently, are easy to design and implement.<>
Keywords :
Gaussian processes; Volterra series; correlation methods; identification; nonlinear systems; signal processing; Gram-Schmidt orthogonalizer; Gram-Schmidt processor; Volterra system identification; correlated Gaussian signals; input signals; memory length; nonlinear processing; nonlinear systems; output elements; signal orthogonalization; truncated Volterra systems; vector; Adaptive filters; Convergence; Kernel; Nonlinear systems; Parameter estimation; Polynomials; Signal design; Signal processing; Statistics; System identification;
Journal_Title :
Signal Processing Letters, IEEE
