A time-dependent LMS algorithm for adaptive filtering

  • Authors:

  • Yuu-Seng Lau;Zahir M. Hussian;Richard Harris

  • Affiliations:

  • Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia;Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia;Centre for Advanced Technology in Telecommunications, School of Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia

  • Venue:
  • ICECS'03 Proceedings of the 2nd WSEAS International Conference on Electronics, Control and Signal Processing
  • Year:
  • 2003

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Abstract

A novel approach for the least-mean-square (LMS) estimation algorithm is proposed. The approach utilizes the conventional LMS algorithm with a time-varying convergence parameter µn rather than a fixed convergence parameter µ. It is shown that the proposed time-varying LMS algorithm (TVLMS) provides reduced mean-squared error and also leads to a faster convergence as compared to the conventional fixed parameter LMS algorithm. This paper presents a performance study for the proposed TV-LMS algorithm and other two main adaptive approaches: the least-mean square (LMS) algorithm and the recursive least-squares (RLS) algorithm. These algorithms have been tested for noise reduction and estimation in single-tone sinusoids and nonlinear narrow-band FM signals corrupted by additive white Gaussian noise. The study shows that the TV-LMS algorithm has a computation time close to conventional LMS algorithm with the advantages of faster convergence time and reduced mean-squared error.