A study on normalized LMS algorithm using refined filtering technique

  • Authors:

  • Yusuke Tsuda;Tetsuya Shimamura

  • Affiliations:

  • Graduate School of Science and Engineering, Saitama University, Saitama, Japan;Graduate School of Science and Engineering, Saitama University, Saitama, Japan

  • Venue:
  • ISPRA'06 Proceedings of the 5th WSEAS International Conference on Signal Processing, Robotics and Automation
  • Year:
  • 2006

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Abstract

We investigate the convergence behavior of the normalized least mean square (NLMS) algorithm in the structure of a linear transversal filter. At the n-th iteration, the traditional NLMS transversal filter generates the n-th output signal by using linear convolution of the n-th input vector and the n-th coefficient vector. Based on this result, the n-th coefficient vector is updated to the n+1-th coefficient vector. We attempt a refined filtering (RF) approach to the NLMS transversal filter, to generate another output signal by linear convolution of the n-th input vector and the n+1-th coefficient vector. Theoretical analysis and computer simulation demonstrate the effectiveness of the RF technique.