Adaptive Nonlinearity Identification in a Hammerstein System using a Pseudo Coherence Function

Authors:
Kun Shi;Xiaoli Ma;G. Tong Zhou
Affiliations:
School of Electrical and Computer Engineering, Georgia Tech, Atlanta, GA 30332-0250, USA;School of Electrical and Computer Engineering, Georgia Tech, Atlanta, GA 30332-0250, USA;School of Electrical and Computer Engineering, Georgia Tech, Atlanta, GA 30332-0250, USA
Venue:
SSP '07 Proceedings of the 2007 IEEE/SP 14th Workshop on Statistical Signal Processing
Year:
2007

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Abstract

A Hammerstein system consists of a memoryless nonlinear block followed by a linear time-invariant subsystem. We propose to model or to approximate the memoryless nonlinear block using a linear combination of nonlinear basis functions. We formulate a novel nonlinearity parameter estimation algorithm using a pseudo magnitude squared coherence (MSC) function based criterion. The proposed method carries out nonlinearity identification without knowing the linear block in the Hammerstein system. A low complexity adaptive algorithm is proposed to update the parameter estimates of the nonlinear block. Numerical examples are provided to illustrate the performance of the proposed method.