LMS algorithm with gradient descent filter length

Yuantao Gu, Kun Tang, Huijuan Cui

Abstract: This letter presents a novel variable-length least mean square algorithm, whose filter length is adjusted dynamically along the negative gradient direction of the squared estimation error. Compared with other variable-length algorithms, the proposed algorithm has faster convergence and more robust performance in diverse environments.