Singular Value Decomposition and Improved Frequency Estimation Using Linear Prediction

IEEE Transactions on Acoustics, Speech, and Signal Processing

Volumn 30, Issue 4, 1982, Pages 671-675

  Tufts, Donald W ^a   Kumaresan, Ramdas ^a  

^a UNIVERSITY OF RHODE ISLAND   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LINEAR PREDICTION;

SIGNAL FILTERING AND PREDICTION;

EID: 0020167422     PISSN: 00963518     EISSN: None     Source Type: Journal    
DOI: 10.1109/TASSP.1982.1163927     Document Type: Article

References (21)

1. D. W. Tufts and R. Kumaresan, “Improved spectral resolution,” Proc. IEEE, vol. 61, pp. 419-420, Mar. 1980.
2. D. W. Tufts and R. Kumaresan, “Improved spectral resolution II,” in Proc. ICASSP, Apr. 1980, pp. 592-597.
3. D. W. Tufts and R. Kumaresan, “Frequency estimation of multiple sinusoids: Making linear prediction perform like maximum likelihood,” Proc. IEEE, to be published.
4. R. Kumaresan and D. W. Tufts, “Data adaptive principal component signal processing,” in Proc. 19th IEEE Int. Conf. Decision Contr., Albuquerque, NM, Feb. 10-12, 1982.
5. A. H. Nuttall, “Spectral analysis of a univariate process with bad data points via maximum entropy and linear predictive techniques,” in NUSC Scientific and Engineering Studies, Spectral Estimation, NUSC, New London, CT, Mar. 1976.
6. T. J. Ulrych and R. W. Clayton, “Time series modelling and maximum entropy,” Phys. Earth and Planetary Interiors, vol. 12, pp. 188-200, Aug. 1976.
7. T. L. Henderson, “Geometric methods for determining system poles from transient response,” IEEE Trans. Acoust., Speech, Signal Processing, Oct. 1981.
8. C. L. Lawson and R. J. Hanson, Solving Least-Squares Problems. Englewood Cliffs, NJ: Prentice-Hall, 1974.
9. R. Kumaresan and D. W. Tufts, “Improved spectral resolution III: Efficient realization,” Proc. IEEE, vol. 68, Oct. 1980.
10. F. B. Hildebrand, Introduction to Numerical Analysis. New York: McGraw-Hill, 1956.
11. C. Lanczos, Applied Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1956.
12. S. W. Lang and J. H. McClellan, “Frequency estimation with maximum entropy spectral estimators,” IEEE Trans. Acoust., Speech, Signal Processing, vol. 28, pp. 716-724, Dec. 1980.
13. R. Kumaresan and D. W. Tufts, “Singular value decomposition and spectral analysis,” in Proc. 1st ASSP Workshop Spectral Estimation, Hamilton, Ont., Canada, Aug. 17, 18, 1981, pp. 6. 4.1-6.4.12.
14. R. Kumaresan, “On the zeros of the linear prediction error filter for deterministic signals,” IEEE Trans. Acoust., Speech, Signal Processing, submitted for publication.
15. V. C. Klemma and A. J. Laub, “The singular value decomposition: Its computation and some applications,” IEEE Trans. Automat. Contr., vol. AC-25, pp. 164-176, Apr. 1980.
16. S. M. Kay and S. L. Marple, “Spectrum analysis-A modern perspective,” Proc. IEEE, vol. 69, pp. 1380-1419, Nov. 1981.
17. J. H. Wilkinson, The Algebraic Eigenvalue Problem. Oxford, England: Clarendon, 1965.
18. C. Eckart and G. Young, “The approximation of one matrix by another of lower rank,” Psychometrika, vol. 1, pp. 211-218, 1936.
19. D. C. Rife and R. R. Boorstyn, “Multiple tone parameter estimation from discrete time observations,” Bell Syst. Tech. J.
20. V. F. Pisarenko, “The retrieval of harmonics from a covariance function,” Geophys. J. Roy. Astron. Soc., vol. 33, pp. 247-266.
21. R. Kumaresan and D. W. Tufts, “Improved Pisarenko-type methods in spectral analysis,” IEEE Trans. Acoust., Speech, Signal Processing, submitted for publication.