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
Link To Document :
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