Title :
LMS algorithm with gradient descent filter length
Author :
Gu, Yuantao ; Tang, Kun ; Cui, Huijuan
Author_Institution :
Dept. of Electron. Eng., Tsinghua Univ., Beijing, China
fDate :
3/1/2004 12:00:00 AM
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.
Keywords :
adaptive filters; convergence of numerical methods; gradient methods; least mean squares methods; signal processing; LMS algorithm; descent filter length; negative gradient direction; signal processing; variable-length least mean square algorithm; Adaptive filters; Convergence; Cost function; Digital communication; Estimation error; Heuristic algorithms; Least mean square algorithms; Least squares approximation; Robustness; Tail;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2003.822892
