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On discrete Fourier spectrum of randomly modulated signals

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work the problem of characterization of Discrete Fourier Transform (DFT) spectrum of an original complex-valued signal modulated by random fluctuations of amplitude and phase is investigated. It is assumed that the amplitude and phase of signal values at discrete time moments of observations are distorted by adding realizations of independent and identically distributed random variables. The obtained results of theoretical analysis of such distorted signal spectra show that only in the case of amplitude modulation the DFT spectrum of the modulated bounded signal can be similar to the original signal spectrum, although there occur random deviations. On the other hand, if phase modulation is present, then the DFT spectrum of the modulated bounded signal not only shows random deviations but also amplitudes of peaks existing in the original spectrum are diminished, and consequently similarity to the original signal spectrum can be significantly blurred.
Rocznik
Strony
143--152
Opis fizyczny
Bibliogr. 19 poz., wykr.
Twórcy
autor
  • Space Research Centre PAS
Bibliografia
  • Allen M.R., Robertson A.W. (1996) Distinguishing Modulated Oscillations from Coloured Noise in Multivariate Datasets, Climate Dynamics, Vol. 12, No. 11, 775-784.
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  • Bloomfield P. (2000) Fourier Analysis of Time Series: An Introduction, Wiley, New York.
  • Bremaud P. (2002) Mathematical Principles of Signal Processing: Fourier and Wavelet Analysis, Springer Verlag Inc., New York.
  • Brillinger D.R. (1975) Time Series - Data Analysis and Theory, Holt, Rinehart and Winston Inc., New York.
  • Cooley J.W. and Tukey J.W. (1965) An Algorithm for the Machine Calculation of Complex Fourier Series, Mathematics of Computation, Vol. 19, 297-301.
  • Foster G. (1996a) Time Series by Projection I: Statistical Properties of Fourier Analysis, The Astronomical Journal, Vol. 111, No. 1, 541-554.
  • Foster G. (1996b) Time Series by Projection II: Tensor Methods for Time Series Analysis, The Astronomical Journal, Vol. 111, No. 1, 555-566.
  • Gasquet C., Witomski P. (1999) Fourier Analysis and Applications - Filtering, Numerical Computation, Wavelets, Springer Verlag Inc., New York.
  • Hinich M.J. (2003) Detecting Randomly Modulated Pulses in Noise, Signal Processing, Vol. 83, Issue 6, 1349-1352.
  • Koopmans L.H. (1974) Spectral Analysis of Time Series, Academic Press, New York.
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  • Ni X. and Huo X. (2007) Statistical Interpretation of the Importance of Phase Information in Signal and Image Reconstruction, Statistics and Probability Letters, Vol. 77, Issue 4, 447-454.
  • Popiński W. (1997) On Consistency of Discrete Fourier Analysis of Noisy Time Series, Artificial Satellites - Journal of Planetary Geodesy, Vol. 32, No. 3, 131-142.
  • Press W.H., Flannery B.P., Teukolsky S.A., Vetterling W.T. (1992) Numerical Recipes - The Art of Scientific Computing, Cambridge University Press, Cambridge.
  • Singleton R.C. (1969) An Algorithm for Computing the Mixed Radix Fast Fourier Transform, IEEE Transactions on Audio and Electroacoustics, Vol. AU-17, No. 2, 93-103.
  • Speed T.P. (1985) Some Practical and Statistical Aspects of Filtering and Spectrum Estimation, In Price J. F. (Editor), Fourier Techniques and Applications, Plenum Press, New York, 101-118.
  • Vautard R., Ghil M. (1989) Singular Spectrum Analysis in Nonlinear Dynamics with Applications to Paleoclimatic Time Series, Physica D, Vol. 35, 395-424.
  • Walker A.M. (1971) On the Estimation of a Harmonic Component in a Time Series with Stationary Independent Residuals, Biometrika, Vol. 58, No. 1, 21-36.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-00e505c9-481d-4c63-97ce-c33d869bb498
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