A comparison of optimized higher order spectral detection techniques for non-gaussian signals

IEEE Transactions on Signal Processing

Volumn 44, Issue 5, 1996, Pages 1198-1213

  Garth, Lee M ^a   Bresler, Yoram ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BANDWIDTH; FREQUENCY DOMAIN ANALYSIS; OPTIMIZATION; SPECTRUM ANALYSIS; SPURIOUS SIGNAL NOISE; STATISTICAL TESTS;

HIGHER ORDER SPECTRA; NON GAUSSIAN SIGNALS;

SIGNAL DETECTION;

EID: 0030142569     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.502332     Document Type: Article

References (32)

1. Kletter and H. Messer, Suhoptimal detection of non-Gaussian signals by third-order spectral analysis, IEEE Trans. Acou.il., Speech, Signal Processing, vol. 38, pp. 901-909, June 1990
2. M J. Hinich and G. R. Wilson, Detection of non-Gaussian signals innon-Gaussian noise using the bispectrum, IEEE Trans. Acoust., Speech, Signal Processing, vol. 38, pp. 1126-1130, July 1990
3. G. B. Giaimakis and M. K. Tsatsanis. Signal detection and classification using matched filtering and higher order statistics,'' IEEE Trans. Acousî.,Speech, Signal Processing, vol. 38, pp. 1284-1296, July 1990
4. G. L. Morclla, A review of signal detection using the hispcctrum with applications in underwater acoustics, Tech. Rep. TR 94-03, Applied Res. Lah., Penn. State Univ., State College, PA, Jan. 1994
5. J. K. Tugnait, Detection of non-Gaussian signals using integratedpolyspectrum, lEEETmns. Signal Processing, vol. 42, pp. 3137-3149.Nov. 1994
6. T. Subba Rao and M. M. Gabr, A test for linearity of stationary timseries, J. Time Series Anal, vol. 1, no. 2, pp. 145-158, 1980
7. M. J. Hinich. Testing for Gaussianity and linearity of a stationary timseries, J. Time Series Anal., vol. 3, no. 3, pp. 169-176, 1982
8. R. A. Ashley, D. M. Patterson, and M. J. Hinich. A diagnostic test for nonlinear serial dependence in time series fitting errors, J. Time SeriesAnal., vol. 7, no. 3, pp. 165-178, 1986
9. T. W. Epps, 'Testing that a stationary time series is Gaussian. Ann. Slat., vol. 15, no. 4, pp. 1683-1698. 1987
10. A. M. Zoubir, Gaussianity test for zero-skewed real and complex data, in Proc. IEEE Signal Processing Workshop Higher Order Slat., LakeTahoe, CA, June 7-9, 1993, pp. 327-331
11. E. Moulines, J. W. Dalle Molle, K. Choukri, and M. Charbit, 'Testing that a stationary time-scries is Gaussian: Time-domain vs. frequencydomain approaches, in Proc. IEEE Signal Processing Workshop HigherOnlerStat., Lake Tahoe, CA. June 7-9, 1993, pp. 336-340
12. G. B. Giannakis and M. K. Tsatsanis, Time-domain tests for Gaussianity and time-reversibility, IEEE Trans. Signal Processing, vol. SP-42, pp. 3460-3472, Dec. 1994
13. L. M. Garth and Y. liresler, On the use of asymptotic in detection and estimation, IEEE Trans. Signa! Processing, vol. 44, pp. 1304-1306,this issue
14. E. Sangfelt and L. Persson, Experimental performance of some higherorder cumulant detectors for hydroacoustic transients, in Proc. IEEE Signal Processing Workshop Higher-Order Slal., Lake Tahoe, CA, June7-9. 1993, pp. 182-186
15. D. R. rillinger and M. Rosenblatt, Asymptotic theory of estimates ofJ.th order spectra, in Spectra! Analysis of Time Series, B. Harris. Ed. New York: VViley, 1967, pp. 153-188
16. M. Rosenhlatt, Stationary Sequences andRamlom Eields. Boston, MA:Birkhauser, 1985
17. I). R. Brillinger and M. Rosenblatt, Computation and interpretation ofk th order spectra, in Spectral Analysis of Time Series, B. Harris. Ed. New York: Wiley. 1967, pp. 189-232
18. V. Chandran and S. Elgar, A general procedure for the derivation of principal domains of higher-order spectra, IEEE Trans. Signal Processing, vol. 42. pp. 229-233. Jan. 1994
19. M. J. Hinich and H. Messer. On the principal domain of the discretebispcctrum of a stationary signal, IEEE Trans. Signal Processing, vol.43, pp. 2130-2134, Sept. 1995
20. T. Subba Rao, The bispectral analysis of nonlinear stationary time series with reference to bilinear time series models, in Handbook ofStatistics, P. R, Krishnaiah, Ed. Amsterdam, The Netherlands: NorthHolland, 1981, vol. 3, chap. 14
21. K. S. Lii and K. N. Heiland, Cross-bispectrtim computation and variance estimation, ACM Trans. Math. Software, vol. 7. pp. 284-294, Sept. 1981
22. C. L. Nikias and M. R. Raghuveer, Bispectrum estimation: A digital signal processing framework, Proc. IEEE, vol. 75, pp. 869-891, July1987.
23. G. L. Wise and C. B. Hall. A comment on the finite memorof stochastic processes, IEEE Trans. Signal Processing, vol. '10p. 2368, Sept. 1992
24. P. Billingsley, Probability and Measure. 3rd eel. New York: Wiley1995
25. D. M. Keenan, Limiting behavior of functionals of higher-order samplcumulant spectra. Ann. Statist., vol. 15, no. 1, pp. 134-151, 1987
26. I,. M. Garfh and Y. Bresler. A comparison of optimized higher-ordedetection techniques, in Proc. Twenty-Eighth Ann. Conf. Inform. Sei5>i(., Princeton Univ., Princeton, NJ. Mar. 16-18, 1994
27. I. Sharfer and H. Messer. The bispectrum of sampled data: Part IDetection of the sampling jitter, Ih.EE Trans. Signal Processing, vol41. pp. 296-312, Jan. 1993
28. N. (jiri, On the complex analogues of T2- and 7?2-tcsts, Ann. MathStatist., vol. 36, pp. 664-670, 1965
29. T. W. Anderson. An Introduction to Multivariase Statistical AnalysisNe York: Wiley, 1984, 2nd ed
30. L. M. Garth. H. V. Poor, and Y. Bresler, Quadratic and higher-ordedetectors for non-Gaussian signals, Hnal Sei. 'lech. Rep., TechnoScicnccs. Inc., Lanham, MD, Spring 1995. (Prepared under SBIContract no. N66001-91-C-7017 for NOSC, San Diego, CA.)
31. O. F.. Barndorff-Nielsen and D. R. Cox, Asymptotic Techniques for Usin Statistics. London, U.K.: Chapman and Hall, 1989
32. L. M. Garth and Y. Bresler, The degradation of higher-order spectradetection using narrowband processing. IEEE Trans. Signal Processingsubmitted for publication.