Open AccessMulti‐stage least mean square algorithm based on polynomial fitting in time‐varying systems
Author(s) -
Wu Guoan,
Liang Jinxin,
Li Wenguang
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2017.4330
Subject(s) - polynomial , convergence (economics) , algorithm , mathematics , least mean squares filter , square (algebra) , adaptive filter , filter (signal processing) , computer science , mathematical analysis , geometry , economics , computer vision , economic growth
A multi‐stage least mean square (MLMS) algorithm based on polynomial fitting in time‐varying systems is proposed. As the name implies, the MLMS algorithm is divided into different stages in sequence. In non‐stationary environments, the optimal weight coefficients of the adaptive filter are variable with time and they are assumed to be approximated by a polynomial. According to the conditions above, the convergence of the MLMS algorithm is verified through theoretical analysis and formula derivation. Similar to the least mean square (LMS) algorithm, the step size μ must be chosen in the specific range to guarantee the feasibility and effectiveness of the MLMS algorithm. Compared with the LMS, LMF and NLMS algorithms, the MLMS algorithm provides the best convergence performance in the simulation experiments.