Improving the initial convergence of adaptive filters: variable-length LMS algorithms

Abstract

Despite its qualities of robustness, low cost, and good tracking performance, in many situations the LMS algorithm suffers from slow initial convergence. We propose a method to speed up this convergence rate by varying the length of the adaptive filter, taking advantage of the larger step-sizes allowed for short filters. The results presented here show that variable-length adaptive filters have the potential to achieve quite fast convergence rates, with a modest increase in the computational complexity.