计算机应用 ›› 2012, Vol. 32 ›› Issue (07): 2078-2081.DOI: 10.3724/SP.J.1087.2012.02078

• 典型应用 • 上一篇    下一篇

改进的最小均方自适应滤波算法

汪成曦1,刘以安1,张强2   

  1. 1. 江南大学 物联网工程学院,江苏 无锡214122
    2. 中国船舶重工集团公司 第七二三研究所,江苏 扬州225001
  • 收稿日期:2011-12-15 修回日期:2012-02-08 发布日期:2012-07-05 出版日期:2012-07-01
  • 通讯作者: 汪成曦
  • 作者简介:汪成曦(1987-),男,安徽太湖人,硕士研究生,主要研究方向:模式识别、信息融合、雷达对抗;刘以安(1963-),男,江苏涟水人,教授,博士,主要研究方向:数据融合、雷达对抗、模式识别、智能系统;张强(1964-),男,江苏扬州人,研究员,主要研究方向:电子对抗、雷达总体设计。

Improved least mean square adaptive filter algorithm

WANG Cheng-xi1,LIU Yi-an1,ZHANG Qiang2   

  1. 1. School of Internet of Things Engineering, Jiangnan University, Wuxi Jiangsu 214122, China
    2. No. 723 Institute, China Shipbuilding Industry Corporation, Yangzhou Jiangsu 225001,China
  • Received:2011-12-15 Revised:2012-02-08 Online:2012-07-05 Published:2012-07-01
  • Contact: WANG Cheng-xi

摘要: 针对传统的固定步长最小均方(LMS)算法应用于雷达杂波自适应滤波器系统存在收敛速度与收敛精确度相矛盾的问题,提出一种新的变步长LMS自适应滤波算法。在其基础步长迭代公式中,通过组合自相关误差与前一步长因子来实时更新迭代下一步长因子的方法,达到具有较快的收敛速度和较小的失调,并且不受已经存在的不相关噪声的干扰的效果。仿真结果表明,所提方法的实验效果与传统固定步长LMS算法及已有算法相比,在收敛速率、收敛精度、抑制噪声方面都有很大的改善,证明所提算法是有效、可行的,且与理论分析一致。

关键词: 最小均方算法, 自适应滤波器, 杂波抑制, 相关噪声, 仿真

Abstract: Concerning the contradiction between convergence speed and convergence precision when the traditional fixed pace Least Mean Square (LMS) algorithm was used to radar clutter adaptive filter system, the paper put forward a new kind of variable-pace adaptive filter algorithm. Through combining the relevant error and the former pace to real-time update next iteration of the pace in its basic pace iterative formula, which could reach with higher convergence speed and smaller disorder, and it also could prevent the bad effect from the existing related noise. The simulation results show that, compared with the traditional fixed-pace LMS algorithm and context improved algorithm, the convergence rate, convergence accuracy and noise prevention have been greatly improved. It proves that the proposed algorithm is effective, feasible, and consistent with the theoretical analysis.

Key words: Least Mean Square (LMS) algorithm, adaptive filter, clutter suppression, related noise, simulation

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