Multi-stage least mean square algorithm based on polynomial fitting in time-varying systems

作者：Wu, Guoan; Liang, Jinxin; Li, Wenguang^*

来源：Electronics Letters, 2018, 54(4): 241-242.

DOI：10.1049/el.2017.4330

• least mean squares methods
• polynomial approximation
• time-varying systems
• adaptive filters
• convergence of numerical methods
• multistage LMS algorithm
• polynomial fitting
• nonstationary environments
• adaptive filter optimal weight coefficients
• MLMS algorithm convergence
• theoretical analysis
• formula derivation

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 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.

• 出版日期2018-2-22
• 单位华中科技大学