The performance of the least-mean-square (LMS) algorithm is governed by its step-size parameter. In this paper, we present a family of optimized LMS-based algorithms (in terms of the step-size control), in the context of system identification. A time-variant system model is considered and the optimization criterion is based on the minimization of the system misalignment. Simulations performed in the context of acoustic echo cancellation indicate that these algorithms achieve a proper compromise in terms of fast convergence/tracking and low misadjustment.
['Silviu Ciochina', 'Constantin Paleologu', 'Jacob Benesty', 'Steven L. Grant', 'Andrei Anghel']
A family of optimized LMS-based algorithms for system identification