ROBUST FXLMS ALGORITHMS WITH IMPROVED CONVERGENCE PERFORMANCE

This paper proposes two modifications of the filtered x least mean squ ares (FxLMS) algorithm with improved convergence behavior albeit at th e same computational cost of 2M operations per time step as the origin al FxLMS update, The paper further introduces a generalized FxLMS recu rsion and establishes that the various algorithms are all of filtered- error form, A choice of the stepsize parameter that guarantees faster convergence and conditions for robustness are also derived, Several si mulation results are included to illustrate the discussions.