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A highly accurate DFT-based parameter estimator for complex exponentials

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Języki publikacji
EN
Abstrakty
EN
A highly accurate DFT-based complex exponential parameter estimation algorithm is presented in this paper. It will be shown that for large number of samples and high signal to noise ratio (SNR), the phase estimation error variance performance is only 0.0475 dB above the Cramer-Rao lower bound (CRLB) for phase estimation with unknown frequency and phase. The amplitude estimation error variance performance was found to lay on the CRLB for amplitude estimation. Exact phase and amplitude estimation can be achieved in the noiseless case with this algorithm. The algorithm has low implementation computational complexity and is suitable for numerous real time digital signal processing applications.
Rocznik
Tom
Strony
76--82
Opis fizyczny
Bibliogr. 13 poz., il.
Twórcy
autor
  • Faculty of Engineering, Cooperative Research Center for Satellite Systems, University of Technology, Sydney, PO Box 123, Broadway, NSW 2007, Australia, jtsui@eng.uts.edu.au
Bibliografia
  • [1] S. Reisenfeld, “A highly accurate algorithm for the estimation of the frequency of a complex exponential in additive Gaussian noise”, in 5th Austr. Commun. Theory Worksh., Newcastle, Australia, 2004, pp. 154–158.
  • [2] S. Reisenfeld and E. Aboutanios, “A new algorithm for the estima-tion of the frequency of a complex exponential in additive Gaus-sian noise”, IEEE Commun. Lett., vol. 7, issue 11, pp. 529–551, 2003.
  • [3] D. C. Rife and R. Boorstyn, “Single tone parameter estimation from discrete-time observations”, IEEE Trans. Inform. Theory, vol. IT-20, no. 5, pp. 591–598, 1974.
  • [4] S. Kay, “A fast and accurate signal frequency estimator”, IEEE Trans. Acoust., Speech Sig. Proces., vol. 37, no. 12, pp. 1987–1990, 1989.
  • [5] R. Schmidt, “Multiple emitter location and signal parameter estima-tion”, IEEE Trans. Anten. Propagat., vol. 34, no. 3, pp. 276–290, 1986.
  • [6] R. Roy and T. Kailath, “ESPRIT – estimation of signal parameters via rotational invariance techniques”, IEEE Trans. Acoust., Speech Sig. Proces., vol. 37, no. 7, pp. 984–995, 1989.
  • [7] S. M. Kay, Fundamentals of Statistical Signal Processing Estimation Theory. Upper Saddle River: Prentice Hall, 1993.
  • [8] Problems in Probability Theory, Mathematical Statistics and Theory of Random Functions, A. A. Sveshnikov, Ed. New York: Dover, 1968.
  • [9] B. James, B. D. O. Anderson, and R. C. Williamson, “Charac-terization of threshold for single tone maximum likelihood fre-quency estimation”, IEEE Trans. Sig. Proces., vol. 43, pp. 817–821, 1995.
  • [10] A. O. Steinhardt and C. Bretherton, “Thresholds in frequency estimation”, in Proc. ICASSP, Tampa, USA, 1985, vol. 10, pp. 1273–1276.
  • [11] B. G. Quinn and P. J. Kootsookos, “Threshold behavior of the max-imum likelihood estimator of frequency”, IEEE Trans. Sig. Proces., vol. 42, pp. 3291–3294, 1994.
  • [12] L. Knockaert, “The Barankin bound and threshold behavior in frequency estimation”, IEEE Trans. Sig. Proces., vol. 45, pp. 2398–2401, 1997.
  • [13] D. G. Manolakis, V. K. Ingle, and S. M. Kogon, Statistical and Adaptive Signal Processing – Spectral Estimation, Signal Modelling, Adaptive Filtering and Array Processing. Boston: McGraw Hill, 2000.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT3-0035-0034
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