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System identification techniques for adaptive signal processing

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Abstract

Many problems in adaptive filtering can be approached from the point of view of system identification. The close interconnection between these two disciplines is explored in some detail. This approach makes it possible to apply recursive parameter estimation algorithms to adaptive signal processing. Several examples are discussed including: adaptive line enhancement, generalized adaptive noise cancelling, adaptive deconvolution and adaptive TDOA estimation. It is shown how the recursive maximum likelihood algorithm can be used for both FIR and IIR filtering, and some preliminary results are presented. Several alternative algorithms are briefly discussed.

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References

  1. L.J. Griffiths, “Rapid Measurement of Digital Instantaneous Frequency,”IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. ASSP-23, No. 2, pp. 207–222, April 1975.

    Google Scholar 

  2. J.R. Zeidler, E.H. Satorius, D.M. Chabries and H.T. Wexler, “Adaptive Enhancement of Multiple Sinosoids in Uncorrelated Noise,”IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. ASSP-26, No. 3, pp. 240–254, June 1978.

    Google Scholar 

  3. J.R. Treichler, “The Spectral Line Enhancer — The Concept, An Implementation and An Application,” Ph.D. dissertation, Stanford University, June 1977.

  4. J.R. Treichler, “Transient and Convergent Behavior of the Adaptive Line Enhancer,”IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. ASSP-27, No. 1, pp. 53–62, February 1979.

    Google Scholar 

  5. J.R. Treichler, “Response of the Adaptive Line Enhancer to Chirped Sinosoids,” Proceedings of the 17th Conference on Decision and Control, San Diego, California, February 1979.

  6. A. Nehorai and D. Malah, “On the Stability and Performance of the Adaptive Line Enhancer,” Proceedings of the Intl. Conf. on Acoustics, Speech and Signal Processing, Denver, Colorado, April 1980.

  7. K.J. Aström and P. Eykhoff, “System Identification — A Survey,”Automatica, Vol. 7, pp. 123–162, 1971.

    Google Scholar 

  8. IEEE Transactions on Automatic Control, Special Issue on System Identification and Time-Series Analysis, December 1974.

  9. P. Eykhoff, “System Parameter and State Estimation,” Wiley, London, 1974.

    Google Scholar 

  10. G.C. Goodwin and R.L. Payne,Dynamic System Identification: Experiment Design and Data Analysis, Academic Press, 1977.

  11. T. Söderström, L. Ljung and I. Gustavsson, “A Comparative Study of Recursive Identification Methods,” Report 7427, Dept. Automatic Control, Lund, Sweden, 1974.

    Google Scholar 

  12. K.J. Åström, U. Borisson, L. Ljung and B Wittenmark, “Theory and Applications of Self-tuning Regulators,”Automatica, Vol. 13, No. 5, pp. 457–476, September 1977.

    Google Scholar 

  13. K.J. Åström and B. Wittenmark, “On Self-tuning Regulators,”Automatica, Vol. 9, No. 2, pp. 185–199, March 1973.

    Google Scholar 

  14. D.W. Clarke and P.J. Gawthrop, “Self-tuning Control,”Proc. IEE, Vol. 126, No. 6, pp. 633–640, June 1979.

    Google Scholar 

  15. B. Egart, “A Unified Approach to Model Reference Adaptive Systems and Selftuning Regulators,” Lund Institute of Technology Report 7134, January 1978.

  16. M.G. Larimore, J.R. Treichler and C.R. Johnson, “SHARF — An Algorithm for Adapting IIR Digital Filters,”IEEE Trans. on Acoustics, Speech and Signal Processing, vol. ASSP-28, No. 4, pp. 428–440, August 1980.

    Google Scholar 

  17. P.L. Feintuch, “An Adaptive Recursive LMS Filter,”Proc. IEEE, Vol. 64, No. 11, pp. 1622–1624, November 1976.

    Google Scholar 

  18. D. Parikh and N. Ahmed, “On An Adaptive Algorithm for IIR Filters,”Proc. IEEE, Vol. 65, No. 5, pp. 585–587, May 1978.

    Google Scholar 

  19. J.R. Treichler, “A Class of Hyperstable Algorithms for Adapting IIR Digital Filters,” Proc. of the International Symposium on Circuits and Systems, Houston, Texas, April 1980.

  20. C.R. Johnson, M.G. Larimore and J.R. Treichler, “SHARF Convergence Properties,”IEEE Trans. on Acoustics, Speech and Signal Processing, vol. ASSP-29, No. 3, pp. 659–669, June 1981.

    Google Scholar 

  21. L. Ljung, “Convergence of an Adaptive Filter Algorithm,”Int. J. Control, Vol.27, No. 5, pp. 673–693, 1978.

    Google Scholar 

  22. P. Hagander and B. Wittenmark, “A Self-tuning Filter for Fixed-Lag Smoothing,”IEEE Trans. on Inf. Theory, Vol. IT-23, No. 3, pp. 377–384, May 1977.

    Google Scholar 

  23. R.K. Mehra, “Approaches to Adaptive Filtering,”IEEE Trans. on Automatic Control, pp. 693–698, October 1972.

  24. L. Ljung, “On Positive Real Functions and the Convergence of Some Recursive Schemes,”IEEE Trans. on Automatic Control, Vol. AC-22, pp. 539–551, 1977.

    Google Scholar 

  25. T. Söderström, L. Ljung and I. Gustavsson, “A Theoretical Analysis of Recursive Identification Methods,”Automatica, Vol. 14, pp. 231–244, 1978.

    Google Scholar 

  26. L. Ljung, “Convergence of Recursive Estimators,” Proceedings of the 5th IFAC Symposium on Identification and System Parameters Estimation, Darmstadt, 1979.

  27. L. Ljung, “Convergence Analysis of Parametric Identification Methods,”IEEE Trans. on Automatic Control, Vol. AC-23, pp. 770–783, 1978.

    Google Scholar 

  28. L. Ljung, “Analysis of Recursive Stochastic Algorithms,”IEEE Trans. on Automatic Control, Vol. AC-22, pp. 551–575, 1977.

    Google Scholar 

  29. V. Solo, “The Convergence of AML,”IEEE Trans. on Automatic Control, Vol. AC-24, No. 6, pp. 958–962, December 1979.

    Google Scholar 

  30. B. Friedlander, “A Modified Pre-Filter for Some Recursive Parameter Estimation Algorithms,”IEEE Trans. Automatic Control, to appear February 1982.

  31. L. Ljung and T. Söderström,Theory and Practice of Recursive Identification, MIT press, 1982, to appear.

  32. B. Friedlander, “A Recursive Maximum Likelihood Algorithm for ARMA Line Enhancement,” Proc. Intl. Conf. Acoustics Speech and Signal Processing, Atlanta, Georgia, March 30–April 1, 1981, pp. 448–491. Also to appear in theIEEE Trans. Acoustics Speech and Signal Processing.

  33. B. Friedlander, “A Recursive Maximum Likelihood Algorithm for ARMA Spectral Estimation,”IEEE Trans. Information Theory, to appear July 1982.

  34. B. Widrow, J. R. Glover, Jr., et al., “Adaptive Noise Cancelling: Principles and Applications,”Proc. IEEE, Vol. 63, pp. 1692–1716, December 1975.

    Google Scholar 

  35. J. R. Glover, Jr., “Adaptive Noise Cancelling Applied to Sinusoidal Interferences,”IEEE Trans. on Acoustics, Speech and Signal Processing, Vol. ASSP-25, No. 6, December 1977.

  36. B. Widrow, J. M. McCool, M. G. Larimore, C. R. Johnson, Jr., “Stationary and Nonstationary Learning Characteristics of the LMS Adaptive Filter,”Proc. IEEE, Vol. 64, No. 8, pp. 1151–1162.

  37. M. Morf and D. Lee, “Recursive Least-Squares Ladder Forms for Fast Parameter Tracking,” Proc. 17th IEEE Conference on Decision and Control, San Diego, California, pp. 1362–1367, January 1979.

  38. B. Friedlander, “System Identification Techniques for Adaptive Noise Cancelling,” Proc. 14th Asilomar Conf. on Circuits, Systems and Computers, Pacific Grove, California, November 1980, pp. 366–370.

    Google Scholar 

  39. B. Friedlander, “An ARMA modeling approach to multitarget tracking,” Proc. 10th IEEE Conf. on Decision and Control, Albuquerque, New Mexico, December 1980.

    Google Scholar 

  40. B. Friedlander, “Multi-Target Tracking Studies: Phase I Final Report,” Systems Control, Inc. Report TM 5334-01, July 1980. Also Phase II Final Report, TM 5334-02, July 1981.

  41. J. D. Markel and A. H. Gray, Jr.,Linear Prediction of Speech, Springer-Verlag, 1976.

  42. L. R. Rabiner and R. W. Schafer,Digital Processing of Speech Signals, Prentice-Hall, Inc., 1978.

  43. B. Friedlander and S. Maitra, “Speech Deconvolution by Recursive ARMA Lattice Filters,” Proc. 1981 Intl. Conf. on Acoustics, Speech and Signal Processing, Atlanta, Georgia, March 30–April 1, 1981, pp. 343–346.

  44. B. Friedlander, “A Modified Lattice Algorithm for Deconvolving Filtered Impulsive Processes,” Proc. 1981 Conf. on Acoustics, Speech and Signal Processing, Atlanta, Georgia, March 30–April 1, 1981, pp. 865–868.

  45. M.T. Silvia and E.A. Robinson,Deconvolution of Geophysical Time Series in the Exploration for Oil and Natural Gas, Elsevier, 1979.

  46. L.C. Wood and S. Treitel, “Seismic Signal Processing,”Proc. IEEE, vol. 63, pp. 649–661, 1975.

    Google Scholar 

  47. D. Godard, “Channel Equalization Using a Kalman Filter for Fast Data Transmission,”IBM J. Res. and Dev., vol. 18, no. 3, pp. 267–273, May 1979.

    Google Scholar 

  48. A. Cantoni and H.B. Doan, “Fast Adaptive Equalization via an Identification Approach,” Proc. Intl. Conf. on Communications, pp. 30.4.1–30.4.4., 1978.

  49. C.H. Knapp and G.C. Carter, “The Generalized Correlation Method for Estimation of Time Delay,”IEEE Trans. On Acoustics, Speech and Signal Processing, vol. ASSP-24, pp. 320–327, August 1976.

    Google Scholar 

  50. W.R. Hahn and S.A. Tretter, “Optimum Processing for Delay-Vector Estimation in Passive Signal Arrays,”IEEE Trans. Inform. Theory, vol. IT-19, pp. 608–614, September 1973.

    Google Scholar 

  51. J.C. Hassab and R.E. Boucher, “Optimum Estimation for Time-Delay by a Generalized Correlator,”IEEE Trans. On Acoustics, Speech, and Signal Processing, vol. ASSP-27, no. 4, pp. 373–380, August 1979.

    Google Scholar 

  52. IEEE Trans. Acoustics, Speech and Signal Processing, Special issue on time delay estimation, June 1981.

  53. G.J. Bierman,Factorization Methods for Discrete Sequential Estimation, Academic Press, 1977.

  54. M.Morf and D.T. Lee, “Fast Algorithms for Speech Modeling,” Final Report to Defense Communication Agency, Contract DCA100-77-C-005, Information Systems Laboratory, Stanford University, November 1978.

  55. M. Morf, “Fast Algorithms for Multivariable System,” Ph.D. dissertation, Stanford University, 1974.

  56. L. Ljung, M. Morf and D. Falconer, “Fast Calculation of Gain Matrices for Recursive Estimation Schemes,”Int. J. Control, vol. 27, no. 1, pp. 1–19, 1978.

    Google Scholar 

  57. M. Morf, T. Kailath and L. Ljung, “Fast Algorithms for Recursive Identification,” Proc. IEEE Conf. on Decision and Control, December 1976.

  58. D.D. Falconer and L. Ljung, “Application of Fast Kalman Estimation to Adaptive Equalization,”IEEE Trans. Comm., vol. COM-26, no. 10, pp. 1439–1446, October 1976.

    Google Scholar 

  59. D.T.L. Lee, M. Morf, and B. Friedlander, “Recursive Least-Squares Ladder Estimation Algorithms,”IEEE Trans. Acoustics Speech and Signal Processing, vol. ASSP-29, no. 3, pp. 627–641, June 1981.

    Google Scholar 

  60. D.T.L. Lee, “Canonical Ladder Form Realizations and Fast Estimation Algorithms,” Ph.D. dissertation, Stanford University, August 1980.

  61. D.T. Lee and M. Morf, “Recursive Square-Root Ladder Estimation Algorithms,” IEEE Intl. Conf. on Acoustics, Speech and Signal Processing, Denver, Colorado, April 1980.

  62. B. Friedlander, “Recursive Lattice Forms for Spectral Estimation and Adaptive Control,” Proc. 19th IEEE Conf. on Decision and Control, Albuquerque, New Mexico, December 1980.

    Google Scholar 

  63. B. Porat, B. Friedlander, and M. Morf, “Square-Root Covariance Ladder Algorithms,” Proc. Conf. on Acoustics, Speech and Signal Processing, Atlanta, Georgia, March 30–April 1, 1981, pp. 877–880.

  64. B. Friedlander, L. Ljung, and M. Morf, “Lattice Implementation of the Recursive Maximum Likelihood Algorithm,” Proc. 20th IEEE Conf. on Decision and Control, San Diego, California, December 1981.

  65. B. Friedlander, “A Pole-Zero Lattice Form for Adaptive Line Enhancement,” Proc. 14th Asilomar Conf. on Circuits, Systems and Computers, Pacific Grove, California, November 1980, pp. 380–384.

    Google Scholar 

  66. B. Friedlander, “Recursive Lattice Forms for Spectral Estimation,” Proc. ASSP Workshop on Spectral Estimation, Hamilton, Ontario, August 1981.

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Authors and Affiliations

  1. Systems Control Technology, Inc., Palo Alto, California

    Benjamin Friedlander

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  1. Benjamin Friedlander
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This work was supported by the Office of Naval Research, Contract No. N00014-79-C-0743.

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Friedlander, B. System identification techniques for adaptive signal processing. Circuits Systems and Signal Process 1, 3–41 (1982). https://doi.org/10.1007/BF01600032

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  • DOI: https://doi.org/10.1007/BF01600032

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