Parameter Estimation of Cyclostationary AM Time Series with Application to Missing Observations

IEEE Transactions on Signal Processing

Volumn 42, Issue 9, 1994, Pages 2408-2419

  Giannakis, Georgios B ^a   Zhou, Guotong ^a  

^a UNIVERSITY OF VIRGINIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AMPLITUDE MODULATION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; RANDOM PROCESSES; REGRESSION ANALYSIS; SIGNAL INTERFERENCE; TIME SERIES ANALYSIS;

ADDITIVE STATIONARY NOISE; CYCLOSTATIONARY AMPLITUDE TIME SERIES; MISSING OBSERVATIONS;

SIGNAL PROCESSING;

EID: 0028496382     PISSN: 1053587X     EISSN: 19410476     Source Type: Journal    
DOI: 10.1109/78.317862     Document Type: Article

References (33)

1. W. R. Bennet, “Statistics of regenerative digital transmission,” Bell Syst. Tech. J., vol. 37, pp. 1501–1542, 1958.
2. A. S. Besicovitch, Almost Periodic Functions. New York: Dover, 1954.
3. B. Besson and F. Castanie, “On estimating the frequency of a sinusoid in auto-regressive multiplicative noise,” Signal Processing, pp. 65–83, Jan. 1993.
4. P. Bloomfield, “Spectral analysis with randomly missing observations,” J. Royal Stat. Soc. B, vol. 32, pp. 369–380, 1970.
5. D. R. Brillinger, Time Series: Data Analysis and Theory. San Francisco: Holden-Day, 1981.
6. A. V. Dandawate, “Exploiting cyclostationarity and higher order statistics in signal processing,” Dept. Elec. Eng., Univ. of Virginia, Jan. 1993.
7. A. V. Dandawate and G. B. Giannakis, “Statistical tests for presence of cyclostationarity,” IEEE Trans. Signal Processing, this issue, pp. 2355–2369.
8. A. V. Dandawate and G. B. Giannakis, “Nonparametric polyspectral estimation of AM signals and processes with missing observations,” IEEE Trans. Inform. Theory, vol. 39, pp. 1864–1876, Nov. 1993.
9. A. V. Dandawate and G. B. Giannakis, “Nonparametric polyspectral estimators for k th-order (almost) cyclostationary processes,” IEEE Trans. Inform. Theory, vol. 40, pp. 67–84, Jan. 1994.
10. R. F. Dwyer, “The use of the kurtosis statistic in the frequency domain as an aid in detecting random signals,” IEEE Trans. Oceanic Eng., vol. OE-10, pp. 303–315, 1984.
11. R. F. Dwyer, “Fourth-order spectra of Gaussian amplitude modulated sinusoids,” J. Acoust. Soc. Amer., vol. 90, pp. 918–926, 1991.
12. B. Friedlander and B. Porat, “Asymptotically optimal estimation of MA and ARMA parameters of non-Gaussian processes from higher-order moments,” IEEE Trans. Automat. Contr., pp. 27–35, Jan. 1990.
13. W. A. Gardner, Statistical Spectral Analysis: A Non-Probabilistic Theory. Englewood Cliffs, NJ: Prentice Hall, 1988.
14. W. A. Gardner, “Exploitation of spectral redundancy in cyclostationary signals,” IEEE Signal Processing Mag., pp. 14–36, Apr. 1991.
15. W. A. Gardner and C. M. Spooner, “Higher-order cyclostationarity, cyclic cumulants, and cyclic polyspectra,” in Proc. Int. Symp. Inform. Theory, Appl. (Hawaii), Nov. 1990.
16. G. B. Giannakis and A. Swami, “Identifiability of general ARMA processes using linear cumulant based estimators,” Automatica, vol. 38, pp. 771–779, July 1992; see also IEEE Trans. Acoust., Speech, Signal Processing, pp. 478–495, 1990.
17. G. B. Giannakis and M. K. Tsatsanis, “A unifying maximum-likelihood view of cumulant and polyspectral measures for non-Gaussian signal classification and estimation,” IEEE Trans. Inform. Theory, vol. 38, pp. 386–406, Mar. 1992.
18. G. B. Giannakis and G. Zhou, “Retrieval of random amplitude modulated harmonics using cyclic statistics,” in Proc. 27th Conf. Inform. Sci., Syst. (Johns Hopkins Univ., Baltimore), Mar. 1993, pp. 650–655.
19. G. B. Giannakis and G. Zhou, “On amplitude modulated time series, higher-order statistics and cyclostationarity,” in Higher-Order Statistical Signal Processing and Applications (E. Powers et al., Eds.). New York: Longman Chesire, 1994.
20. M. J. Hinich, “Detecting a hidden periodic signal when its period is unknown,” IEEE Trans. Acoust., Speech., Signal Processing, vol. ASSP-30, pp. 747–750, 1982.
21. H. L. Hurd and N. L. Gerr, “Graphical methods for determining the presence of periodic correlation,” J. Time Series, vol. 12, no. 4, pp. 337–350, 1991.
22. R. H. Jones, “Spectral analysis with regularly missed observations,” Ann. Math. Stat., vol. 33, pp. 455–461, 1962.
23. R. H. Jones, “Maximum likelihood fitting of ARMA models to time series with missing observations,” Technometrics, vol. 22, no. 3, pp. 389–395, Aug. 1980.
24. D. G. Luenberger, Linear and Nonlinear Programming. Reading, MA: Addison-Wesley, 1984, 2nd ed.
25. I. Jouny, “On the bispectrum of complex signals,” in Proc. Int. Signal Processing Workshop on Higher-Order Stat. (Chamrousse, France), July 1991, pp. 17–20.
26. W. D. Mark, “Effects of intensity modulations on the power spectra of random processes,” J. Sound, Vibrat., vol. 119, pp. 451–487, 1987.
27. J. M. Mendel, “Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications,” Proc. IEEE, vol. 79, no. 3, pp. 278–305, Mar. 1991.
28. E. Parzen, “On spectral analysis with missing observations and amplitude modulation,” Sankhya, ser. 4, vol. 25, pp. 383–392, 1963.
29. B. Porat and B. Friedlander, “ARMA spectral estimation of time series with missing observations,” IEEE Trans. Inform. Theory, vol. IT-30, no. 6, pp. 823–831, Nov. 1984.
30. Y. Rosen and B. Porat, “Optimal ARMA parameter estimation based on the sample covariances for data with missing observations,” IEEE Trans. Inform. Theory, vol. 35, no. 2, pp. 334–341, Mar. 1989.
31. A. Swami, “Multiplicative noise models: parameter estimation using cumulants,” in Proc. 5th ASSP Workshop Spectral Estimvt., Modeling, Oct. 1990, 212–216.
32. A. Swami, G. B. Giannakis, and S. Shamsunder, “Multichannel ARMA processes,” IEEE Trans. Signal Processing, vol. 42, no. 4, pp. 898–913, Apr. 1994.
33. R. Urick, “Models for amplitude fluctuations of narrowband signals and noise in the sea,” J. Acoust. Soc. Amer., vol. 62, pp. 878–887, 1977.