Advertisement

Fundamentals of Adaptive Filtering

  • Paulo Sergio Ramirez Diniz
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 399)

Abstract

This chapter includes a brief review of deterministic and random signal representations. Due to the extent of those subjects our review is limited to the concepts that are directly relevant to adaptive filtering. The properties of the correlation matrix of the input signal vector are investigated in some detail, since they play a key role in the statistical analysis of the adaptive filtering algorithms.

Keywords

Input Signal Adaptive Filter Random Signal Unknown System Digital Subscriber Line 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. G. Luenberger, Introduction to Linear and Nonlinear Programming, Addison Wesley, Reading, MA, 1973.zbMATHGoogle Scholar
  2. 2.
    R. Fletcher, Practical Methods of Optimization, John Wiley & Sons, New York, NY, 2nd edition, 1990.Google Scholar
  3. 3.
    B. Widrow and M. E. Hoff, “Adaptive switching circuits,” WESCOM Conv. Rec., pt. 4, pp. 96–140, 1960.Google Scholar
  4. 4.
    B. Widrow, J. M. McCool, M. G. Larimore, and C. R. Johnson, Jr., “Stationary and nonstationary learning characteristics of the LMS adaptive filters,” Proceedings of the IEEE, vol. 64, pp. 1151–1162, Aug. 1976.MathSciNetCrossRefGoogle Scholar
  5. 5.
    A. Papoulis, Signal Analysis, McGraw Hill, New York, NY, 1977.zbMATHGoogle Scholar
  6. 6.
    A. V. Oppenheim, A. S. Willsky, and I. T. Young, Signals and Systems, Prentice Hall, Englewood Cliffs, NJ, 1983.zbMATHGoogle Scholar
  7. 7.
    A. V. Oppenheim and R. W. Schaffer, Discrete-Time Signal Processing, Prentice Hall, Englewood Cliffs, NJ, 1989.zbMATHGoogle Scholar
  8. 8.
    A. Antoniou, Digital Filters: Analysis, Design, and Applications, McGraw Hill, New York, NY, 2nd edition, 1992.Google Scholar
  9. 9.
    L. B. Jackson, Digital Filters and Signal Processing, Kluwer Academic Publishers, Norwell, MA, 3rd edition, 1996.Google Scholar
  10. 10.
    R. A. Roberts and C. T. Mullis, Digital Signal Processing, Addison-Wesley, Reading, MA, 1987.zbMATHGoogle Scholar
  11. 11.
    J. G. Proakis and D. G. Manolakis, Digital Signal Processing, Macmillan Publishing Company, New York, NY, 2nd edition, 1992.zbMATHGoogle Scholar
  12. 12.
    M. Beilanger, Digital Processing of Signals, John Wiley & Sons, New York, NY, 1984.Google Scholar
  13. 13.
    A. Papoulis, Probability, Random Variables, and Stochastic Processes, McGraw Hill, New York, NY, 3rd edition, 1991.Google Scholar
  14. 14.
    P. Z. Peebles, Jr., Probability, Random Variables, and Random Signal Principles, McGraw Hill, New York, NY, 3rd edition, 1993.Google Scholar
  15. 15.
    W. A. Gardner, Introduction to Random Processes, McGraw Hill, New York, NY, Second edition, 1990.Google Scholar
  16. 16.
    C. R. Johnson, Jr., Lectures on Adaptive Parameter Estimation, Prentice Hall, Englewood Cliffs, NJ, 1988.zbMATHGoogle Scholar
  17. 17.
    T. Söderström and P. Stoica, System Identification, Prentice Hall International, Hemel Hempstead, Hertfordshire, 1989.zbMATHGoogle Scholar
  18. 18.
    G. Strang, Linear Algebra and Its Applications, Academic Press, New York, NY, 2nd Edition, 1980.Google Scholar
  19. 19.
    A. Papoulis, “Predictable processes and Wold’s decomposition: A review,” IEEE Trans. on Acoust., Speech, and Signal Processing, vol. ASSP-33, pp. 933–938, Aug. 1985.MathSciNetCrossRefGoogle Scholar
  20. 20.
    S. M. Kay, Modern Spectral Analysis, Prentice Hall, Englewood Cliffs, NJ, 1988.Google Scholar
  21. 21.
    S. L. Marple, Jr., Digital Spectral Analysis, Prentice Hall, Englewood Cliffs, NJ, 1987.Google Scholar
  22. 22.
    M. L. Honig and D. G. Messerschmitt, Adaptive Filters: Structures, Algorithms, and Applications, Kluwer Academic Publishers, Boston, MA, 1984.zbMATHGoogle Scholar
  23. 23.
    B. Widrow and S. D. Stearns, Adaptive Signal Processing, Prentice Hall, Englewood Cliffs, NJ, 1985.zbMATHGoogle Scholar
  24. 24.
    S. T. Alexander, Adaptive Signal Processing, Springer Verlag, New York, NY, 1986.zbMATHCrossRefGoogle Scholar
  25. 25.
    J. R. Treichler, C. R. Johnson, Jr., and M. G. Larimore, Theory and Design of Adaptive Filters, John Wiley & Sons, New York, NY, 1987.zbMATHGoogle Scholar
  26. 26.
    M. Bellanger, Adaptive Digital Filters and Signal Analysis, Marcel Dekker, Inc., New York, NY, 1987.Google Scholar
  27. 27.
    P. Strobach, Linear Prediction Theory, Springer Verlag, New York, NY, 1990.zbMATHCrossRefGoogle Scholar
  28. 28.
    S. Haykin, Adaptive Filter Theory, Prentice Hall, Englewood Cliffs, NJ, 2nd edition, 1991.zbMATHGoogle Scholar
  29. 29.
    S. U. Qureshi, “Adaptive Equalization,” Proceedings of the IEEE, vol. 73, pp. 1349–1387, Sept. 1985.CrossRefGoogle Scholar
  30. 30.
    J. G. Proakis, Digital Communication, McGraw Hill, New York, NY, 2nd edition, 1989.Google Scholar
  31. 31.
    L. C. Wood and S. Treitel, “Seismic signal processing,” Proceedings of the IEEE, vol. 63, pp. 649–661, Dec. 1975.CrossRefGoogle Scholar
  32. 32.
    D. G. Messerschmitt, “Echo cancellation in speech and data transmission,” IEEE Journal on Selected Areas in Communications, vol. SAC-2, pp. 283–296, March 1984.CrossRefGoogle Scholar
  33. 33.
    M. L. Honig, “Echo cancellation of voiceband data signals using recursive least squares and stochastic gradient algorithms,” IEEE Trans. on Communications, vol. COM-33, pp. 65–73, Jan. 1985.CrossRefGoogle Scholar
  34. 34.
    S. Subramanian, D. J. Shpak, P. S. R. Diniz, and A. Antoniou, “The performance of adaptive filtering algorithms in a simulated HDSL environment,” Proc. IEEE Canadian Conf. Electrical and Computer Engineering, Toronto, Canada, pp. TA 2.19.1–TA 2.19.5, Sept. 1992.Google Scholar
  35. 35.
    D. W. Lin, “Minimum mean-squared error echo cancellation and equalization for digital subscriber line transmission: Part I — theory and computation,” IEEE Trans. on Communications, vol. 38, pp. 31–38, Jan. 1990.CrossRefGoogle Scholar
  36. 36.
    D. W. Lin, “Minimum mean-squared error echo cancellation and equalization for digital subscriber line transmission: Part II — a simulation study,” IEEE Trans. on Communications, vol. 38, pp. 39–45, Jan. 1990.CrossRefGoogle Scholar
  37. 37.
    L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals, Prentice Hall, Englewood Cliffs, NJ, 1978.Google Scholar
  38. 38.
    B. D. Van Veen and K. M. Buckley, “Beamforming: a versatile approach to spatial filtering,” IEEE Acoust., Speech, Signal Processing Magazine, vol. 37, pp. 4–24, April 1988.Google Scholar
  39. 39.
    B. Widrow, J. R. Grover, Jr., J. M. McCool, J. Kaunitz, C. S. Williams, R. H. Hearns, J. R. Zeidler, E. Dong, Jr., and R. C. Goodlin, “Adaptive noise cancelling: Principles and applications,” Proceedings of the IEEE, vol. 63, pp. 1692–1716, Dec. 1975.CrossRefGoogle Scholar
  40. 40.
    M. Abdulrahman and D. D. Falconer, “Cyclostationary crosstalk suppression by decision feedback equalization on digital subscriber line,” IEEE Journal on Selected Areas in Communications, vol. 10, pp. 640–649, April 1992.CrossRefGoogle Scholar
  41. 41.
    H. Samueli, B. Daneshrad, R. B. Joshi, B. C. Wong, and H. T. Nicholas, III, “A 64-tap CMOS echo canceller/decision feedback equalizer for 2B1Q HDSL transceiver,” IEEE Journal on Selected Areas in Communications, vol. 9, pp. 839–847, Aug. 1991.CrossRefGoogle Scholar
  42. 42.
    J.-J. Werner, “The HDSL environment,” IEEE Journal on Selected Areas in Communications, vol. 9, pp. 785–800, Aug. 1991.CrossRefGoogle Scholar
  43. 43.
    J. W. Leichleider, “High bit rate digital subscriber lines: A review of HDSL progress,” IEEE Journal on Selected Areas in Communications, vol. 9, pp. 769–784, Aug. 1991.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

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

Personalised recommendations