Fast Transversal RLS Algorithms

  • Paulo Sergio Ramirez
Chapter
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 694)

Abstract

Among the large number of algorithms that solve the least-squares problem in a recursive form, the fast transversal recursive least-squares (FTRLS) algorithms are very attractive due to their reduced computational complexity [1]–[7].

Keywords

Adaptive Filter Finite Precision Forward Prediction Prediction Filter Transversal Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. D. Falconer and L. Ljung, “Application of fast Kalman estimation to adaptive equalization,” IEEE Trans, on Communications, vol. COM-26, pp. 1439–1446, Oct. 1978.CrossRefGoogle Scholar
  2. 2.
    G. Carayannis, D. G. Manolakis, and N. Kalouptsidis, “A fast sequential algorithm for least-squares filtering and prediction,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. ASSP-31, pp. 1394–1402, Dec. 1983.CrossRefGoogle Scholar
  3. 3.
    J. M. Cioffi and T. Kailath, “Fast, recursive-least-squares transversal filters for adaptive filters,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. ASSP-32, pp. 304–337, April 1984.CrossRefGoogle Scholar
  4. 4.
    J. M. Cioffi and T. Kailath, “Windowed fast transversal filters adaptive algorithms with normalization,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. ASSP-33, pp. 607–627, June 1985.CrossRefGoogle Scholar
  5. 5.
    S. Ljung and L. Ljung, “Error propagation properties of recursive least-squares adaptation algorithms,” Automatica, vol. 21, pp. 157–167, 1985.MathSciNetMATHCrossRefGoogle Scholar
  6. 6.
    J.-L. Botto and G. V. Moustakides, “Stabilizing the fast Kalman algorithms,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. 37, pp. 1342–1348, Sept. 1989.MATHCrossRefGoogle Scholar
  7. 7.
    M. Bellanger, “Engineering aspects of fast least squares algorithms in transversal adaptive filters,” Proc. IEEE Intern. Conf. on Acoust., Speech, Signal Processing, pp. 49.14.1–49.14.4, 1987.Google Scholar
  8. 8.
    D. T. M. Slock and T. Kailath, “Fast transversal filters with data sequence weighting,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. 37, pp. 346–359, March 1989.MathSciNetCrossRefGoogle Scholar
  9. 9.
    D. T. M. Slock and T. Kailath, “Numerically stable fast transversal filters for recursive least squares adaptive filtering,” IEEE Trans, on Signal Processing, vol. 39, pp. 92–113, Jan. 1991.MATHCrossRefGoogle Scholar
  10. 10.
    J. G. Proakis, C. M. Rader, F. Ling, and C. L. Nikias, Advanced Digital Signal Processing, MacMillan, New York, NY, 1992.MATHGoogle Scholar
  11. 11.
    B. Toplis and S. Pasupathy, “Tracking improvements in fast RLS algorithms using a variable forgetting factor,” IEEE Trans, on Acoust., Speech, and Signal Processing, vol. 36, pp. 206–227, Feb. 1988.MATHCrossRefGoogle Scholar
  12. 12.
    D. Boudreau and P. Kabal, “Joint-time delay estimation and adaptive recursive least squares filtering,” IEEE Trans, on Signal Processing, vol. 41, pp. 592–601, Feb. 1993.MATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Paulo Sergio Ramirez
    • 1
  1. 1.Federal University of Rio de JaneiroRio de JaneiroBrazil

Personalised recommendations