文档介绍：基于LMS算法的自适应组合滤波器中英文翻译
英文原文
Combined Adaptive Filter with LMS-Based Algorithms
Boˇ zo Krstaji´ c, LJubiˇ sa Stankovi´ c,and Zdravko Uskokovi´
Abstract: bined adaptive filter is proposed. It consists of parallel LMS-based adaptive FIR filters and an algorithm for choosing the better among them. As a criterion parison of the considered algorithms in the proposed filter, we take the ratio between bias and variance of the weighting coefficients. Simulations results confirm the advantages of the proposed adaptive filter.
Keywords: Adaptive filter, LMS algorithm, Combined algorithm,Bias and variance trade-off
Adaptive filters have been applied in signal processing and control, as well as in many practical problems, [1, 2]. Performance of an adaptive filter depends mainly on the algorithm used for updating the filter weighting coefficients. The monly used adaptive systems are those based on the Least Mean Square (LMS) adaptive algorithm and its modifications (LMS-based algorithms).
The LMS is simple for implementation and robust in a number of applications [1–3]. However, since it does not always converge in an acceptable manner, there have been many attempts to improve its performance by the appropriate modifications: sign algorithm (SA) [8], geometric mean LMS (GLMS) [5], variable step-size LMS(VS LMS) [6, 7].
Each of the LMS-based algorithms has at least one parameter that should be defined prior to the adaptation procedure (step for LMS and SA; step and smoothing coefficients for GLMS; various parameters affecting the step for VS LMS). These parameters crucially influence the filter output during two adaptation phases:transient and steady state. Choice of these parameters is mostly based on some kind of trade-off between the quality of algorithm performance in the mentioned adaptation phases.
We propose a possible approach for the LMS-based adaptive filter performance improvement. Namely, we make bination of several LMS-based FIR filters with different parameters,