A new robust adaptive step size LMS algorithm

Abstract

In this contribution a new robust technique for adjusting the step size of the Least Mean Squares (LMS) adaptive algorithm is introduced. The proposed method exhibits faster convergence, enhanced tracking ability and lower steady state excess error compared to the fixed step size LMS and other previously developed variable step size algorithms, while retaining much of the LMS computational simplicity. A theoretical behaviour analysis is conducted and equations regarding the evolution of the weight error vector correlation matrix together with convergence bounds are established. Extensive simulation results support the theoretical analysis and confirm the desirable characteristics of the proposed algorithm.