Mean weight behavior of the filtered-X LMS algorithm

Abstract

A new stochastic analysis is presented for the filtered-X LMS (FXLMS) algorithm. The analysis does not use independence theory. An analytical model is derived for the mean behavior of the adaptive weights. The model is valid for white or colored reference inputs and accurately predicts the mean weight behavior even for large step sizes. The constrained Wiener solution is determined as a function of the input statistics and the impulse responses of the adaptation loop filters. Effects of secondary path estimation error are studied. Monte Carlo simulations demonstrate the accuracy of the theoretical model.