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Application of the bisspectrum functions to the measuring data analysis
Języki publikacji
Abstrakty
W pracy omówiono zastosowanie funkcji bispektrum oraz metod statystyki wielowymiarowej do analizy danych pomiarowych ewidencjonowanych w warunkach oddziaływania niekontrolowanych zakłóceń na środowisko pomiarowe.
When dealing with signals that exhibit irregular behavior, the most widely accepted approach consists of modelling it as the realization of some stochastic process. Because of the nature of these classes of signals, higher order techniques are likely to play an important role in algorithms aimed at processing them in the measuring systems. The higher order spectra (also known as polyspectra), defined in terms of higher order statistics (cumulants) of a signal are atractive since no special assumptions on the underlying signal model are necessary. There are several general motivations behind the use of the higher order spectra in signal processing. These include techniques to: (1) suppress additive colored Gaussian noise of unknown power spectrum; (2) identify nonminimum phase systems or reconstruct nonminimum phase signals; (3) extract information due to deviations from Gaussianity; (4) detect and characterize nonlinear properties in signals as well as identify nonlinear systems. The particular cases of the higher order spectra is the third order spectrum also called the bispectrum, which is the Fourier transform of the third order statistics. This paper is devoted to the study of bispectrum for explorative analysis of the measuring data, collected in the conditions, when measuring signals are corrupted by nonidentiflcable disturbations in measuring process. The algorithm based on multidimensional analysis measuring data has been proposed. The deciles of the distribution of bispectrum in their principal domain have been used as distinctive features for data set classification. An example of data analysis collected in underwater environment has been discussed.
Wydawca
Czasopismo
Rocznik
Tom
Strony
48--57
Opis fizyczny
Bibliogr. 11 poz., tab., wykr.
Twórcy
autor
- Akademia Marynarki Wojennej
Bibliografia
- [1] Badźmirowski K., Karkowska H., Karkowski Z.: Cyfrowe systemy pomiarowe, WNT, Warszawa 1979.
- [2] Brocket P.L., Hinich M., Wilson G.R.: Nonlinear and non-Gaussian Ocean Noise, JASA 1987, vol 82, no 4, s. 1386-1394.
- [3] Hand D.J.: Discrimination and classification, John Wiley & Sons, New York, 1981, s. 218.
- [4] Hinich M., Marandino D., Sullivan E.: Bispectrum of ship-radiated noise, JASA, 1989, vol. 85, no 4, str. 1512-1517.
- [5] Kiciński W.: Problemy pomiarów szumów podwodnych, Prace PIE, 2000, nr 140, s. 111.
- [6] Kiciński W., Baranowska A.: An approach to signal pre-processing in underwater noise measurements. 7th International Congress on Sound and Vibration, Garnish-Partenkirchen, Germany, 4-7 July 2000, s. 3385-3392.
- [7] Klette D., Messer H.: Suboptimal detection of non-Gaussian signals by third-order spectral analysis, IEEE Trans. on Acoustic Speech and Signal Processing, 1990, vol. 38, no 6, str. 901-909.
- [8] Mendel J .M.: Tutorial on higher-order statistics (spectra) in signal processing and system theory: Theoretical results and some applications, Proc. IEEE, 1991, vol. 79 no 3, s. 278-305.
- [9] Murino V., Ottonello C., Pagman S.: Noisy texture classification: a higher-order statistics approach, Pattern Recognition, 1998, vol. 21, no 4, str. 383-393.
- [10] Nikias C.L., Mendel J.M.: Signal Processing with Higher-Order Spectra, IEEE Signal Processing Magazine, 1993, no 7, s. 10-37.
- [11] Regazzoni C., Tacconi G., Tesel A.: An application to local optimal detection of higher order spectral analysis, Proc. of The Conference of Underwater Defence Technology, 1995, str. 249-293.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA3-0026-0016
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