• DocumentCode
    1440993
  • Title

    The higher order theory of generalized almost-cyclostationary time series

  • Author

    Izzo, Luciano ; Napolitano, Antonio

  • Author_Institution
    Dipt. di Ingegneria Elettronica e delle Telecomunicazioni, Napoli Univ., Italy
  • Volume
    46
  • Issue
    11
  • fYear
    1998
  • fDate
    11/1/1998 12:00:00 AM
  • Firstpage
    2975
  • Lastpage
    2989
  • Abstract
    In this paper, the class of generalized almost-cyclostationary (GACS) time series is introduced. Time series belonging to this class are characterized by multivariate statistical functions that are almost-periodic functions of time whose Fourier series expansions can exhibit coefficients and frequencies depending on the lag shifts of the time series. Moreover, the union over all the lag shifts of the lag-dependent frequency sets is not necessarily countable. Almost-cyclostationary (ACS) time series turn out to be the subclass of GACS time series for which the frequencies do not depend on the lag shifts and the union of the above-mentioned sets is countable. The higher order characterization of GACS time series in the strict and wide sense is provided. It is shown that the characterization in terms of cyclic moment and cumulant functions is inadequate for those GACS time series that are not ACS. Then, generalized cyclic moment and cumulant functions (in both the time and frequency domains) are introduced. Finally, the problem of estimating the introduced generalized cyclic statistics is addressed, and two examples of GACS time series are considered
  • Keywords
    Fourier series; higher order statistics; signal processing; time series; Fourier series expansions; GAC time series; almost-cyclostationary time series; almost-periodic functions; cumulant function; cyclic moment function; generalized almost-cyclostationary time series; generalized cyclic statistics; higher order theory; lag shifts; multivariate statistical functions; Fourier series; Frequency domain analysis; Helium; Higher order statistics; Interference; Noise generators; Parameter estimation; Probability; Signal processing; Stochastic processes;
  • fLanguage
    English
  • Journal_Title
    Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1053-587X
  • Type

    jour

  • DOI
    10.1109/78.726811
  • Filename
    726811