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应用科学学报 ›› 2013, Vol. 31 ›› Issue (5): 475-480.doi: 10.3969/j.issn.0255-8297.2013.05.006

• 信号与信息处理 • 上一篇    下一篇

变步长凸组合LMS自适应滤波算法及分析

苗俊1,2， 芮国胜1， 张洋1   

  1. 1. 海军航空工程学院电子信息工程系, 山东烟台264001
    2. 海军航空工程学院研究生管理大队, 山东烟台264001
  • 收稿日期:2011-09-17 修回日期:2011-12-14 出版日期:2013-09-26 发布日期:2011-12-14
  • 作者简介:苗俊，博士生，研究方向：现代通信系统、自适应信道均衡，E-mail: miaojun115@sina.com；芮国胜，教授，博导，研究方向：现代通信、小波理论及其应用，E-mail: 121941512@qq.com
  • 基金资助:

    “泰山学者”建设工程专项经费资助

Variable Step-Size Convex Combination of LMS Adaptive Filtering: Algorithm and Analysis

MIAO Jun1,2, RUI Guo-sheng1, ZHANG Yang1   

  1. 1. Department of Electronic Information Engineering, Naval Aeronautical and Astronautical University,
    Yantai 264001, Shandong Province, China
    2. Graduate Students’ Brigade, Naval Aeronautical and Astronautical University, Yantai 264001,
    Shandong Province, China
  • Received:2011-09-17 Revised:2011-12-14 Online:2013-09-26 Published:2011-12-14

PDF

1045

被引次数

14

摘要： 针对现有变步长凸组合LMS(VSCLMS)算法需预先决定行为参数的问题，提出了一种新的VSCLMS算法. 该算法对变步长滤波器按步进行分析，以最大均方权值偏差准则为步长选择依据，对固定步长滤波器则采用稳态最小均方误差准则为步长选择依据. 理论分析与仿真实验表明，该算法能在噪声、时变、甚至非平稳的环境下保持良好的随动性能，并在收敛的各个阶段均保持快速而稳定的均方特性. 与现有VSCLMS及CLMS算法相比，具有更快的收敛速度，且稳态均方性能与跟踪性能均更好.

关键词: 自适应滤波, CLMS 算法, VSCLMS 算法, 变步, 凸组合

Abstract: The existing variable step-size convex combination of LMS (VSCLMS) algorithm needs to predetermine the behavioral parameters. To avoid this, the paper proposes a new variable step-size adaptive filter with analytical minimization of the ensemble-averaged mean-square weight error. Instead of variable step-size parameters in the original VSCLMS, the proposed algorithm uses a constant step-size parameter derived based on steady-state MSWE minimization. Theoretical analysis and simulations show that the proposed algorithm has a better tracking performance in the presence of noise and in a time-varying and even non-stable environment. Besides, it converges fast and is stable in the convergence process, and is better in these aspects as compared with the original VSCLMS and CLMS algorithms.

Key words: adaptive filtering, CLMS, VSCLMS, variable step-size, convex combination

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