上海交通大学学报(英文版) ›› 2013, Vol. 18 ›› Issue (6): 742-748.doi: 10.1007/s12204-013-1460-8

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An Improved Proportionate Normalized Least Mean Square Algorithm for Sparse Impulse Response Identification

WEN Hao-xiang* (文昊翔), LAI Xiao-han (赖晓翰), CHEN Long-dao (陈隆道), CAI Zhong-fa (蔡忠法)   

  1. (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)
  • 出版日期:2013-12-31 发布日期:2013-12-18
  • 通讯作者: WEN Hao-xiang (文昊翔) E-mail: xiangxiang_0@163.com

An Improved Proportionate Normalized Least Mean Square Algorithm for Sparse Impulse Response Identification

WEN Hao-xiang* (文昊翔), LAI Xiao-han (赖晓翰), CHEN Long-dao (陈隆道), CAI Zhong-fa (蔡忠法)   

  1. (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)
  • Online:2013-12-31 Published:2013-12-18
  • Contact: WEN Hao-xiang (文昊翔) E-mail: xiangxiang_0@163.com

摘要: In this paper after analyzing the adaptation process of the proportionate normalized least mean square (PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefficient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.

关键词: adaptive algorithm, echo cancellation (EC), proportionate normalized least mean square (PNLMS) algorithm, proportionate step-size, sparse impulse response

Abstract: In this paper after analyzing the adaptation process of the proportionate normalized least mean square (PNLMS) algorithm, a statistical model is obtained to describe the convergence process of each adaptive filter coefficient. Inspired by this result, a modified PNLMS algorithm based on precise magnitude estimate is proposed. The simulation results indicate that in contrast to the traditional PNLMS algorithm, the proposed algorithm achieves faster convergence speed in the initial convergence state and lower misalignment in the stead stage with much less computational complexity.

Key words: adaptive algorithm, echo cancellation (EC), proportionate normalized least mean square (PNLMS) algorithm, proportionate step-size, sparse impulse response

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