Subspace based Nonlinear Multichannel Blind Identification/Equalization

GÓMEZ, JUAN CARLOS; BAEYENS, ENRIQUE; POOLLA, KAMESHWAR

Saint-Malo, France

Simposio; 15th IFAC Symposium on System Identification (SYSID 2009), pp. 1692-1697; 2009

IFAC: International Federation of Automatic Control

A subspace based approach for the blind identification/equalization of nonlinear Single Input Multiple Output (SIMO) time-invariant channels is presented in this paper. The considered models include, but are not limited to, polynomial approximations of nonlinear channels (i.e., Volterra models), which have been used to represent satellite communications, magnetic recording channels, and physiological processes, among other applications. The channel parameters are estimated in closed form, up to a scalar factor, by computing the column space of the output Hankel matrix, via a Singular Value Decomposition (SVD), and then solving a Least Squares (LS) estimation problem also resorting to an SVD. Numerical simulations are used to illustrate the performance of the proposed algorithms.i.e., Volterra models), which have been used to represent satellite communications, magnetic recording channels, and physiological processes, among other applications. The channel parameters are estimated in closed form, up to a scalar factor, by computing the column space of the output Hankel matrix, via a Singular Value Decomposition (SVD), and then solving a Least Squares (LS) estimation problem also resorting to an SVD. Numerical simulations are used to illustrate the performance of the proposed algorithms.