Normalized LMS algorithm with orthogonal correction factors

Abstract

A procedure is presented to accelerate the convergence of the normalized LMS algorithm for colored inputs. The usual NLMS algorithm reduces the distance between the estimated and true system weights, where the correction is in the direction of the input vector. For colored inputs the correction is mostly in the direction of the largest eigenvector. We therefore generate additional, NLMS-like, corrections of the weight vector in directions orthogonal to the input vector and orthogonal to each other. Simulated as well as measurement-based examples show a good acceleration of convergence, especially for high coherence between the input and the desired signal.