Linearization of weakly nonlinear Volterra systems using FIR filters and recursive prediction error method

Linearization of nonlinear systems is a very important topic in many practical applications. The linearization scheme which was suggested in for Volterra systems using adaptive linear and nonlinear FIR filters is considered in this paper. The coefficients of these filters can be recursively estimated using the Least Mean Squares (LMS) algorithm. In this paper, the Recursive Prediction Error Method (RPEM) algorithm is used in order to achieve more accurate estimates and improve the performance of the suggested linearization scheme. Simulation study shows that the RPEM algorithm more significantly suppresses the spectral regrowth and achieves much lower nonlinear distortion than the LMS algorithm.