Fraction of Time Probability That Exhibit Cyclostationarity


A cyclostationary process is signal that have statistical properties. The process can be viewed as multiple interleaved stationary process. This signal is natural extension for the random statistical signal. But with the fraction of time dependence it is limited to cyclical, periodical case. In the sequence of the time variant characteristics cyclostationarity signal are not stationary signals. In some cases, it is taken as special version for the stationary signal.

The cyclostationarity signal work as the bridge between stationary and non-stationary signals. It possesses common strength among the ordinary stationary signals. On basis for the evaluation and the analysis of these signals, the cyclical version of time and frequency has been defined.

The cyclical expression is being kind of generalisation of the stationary ones. It contains usual stationary expressions. In some hard fact facing case only the formal similarity is maintained and cyclical expression has different meaning.

The cyclical signal processing operation allows you so that you can find out new features, you can extend the knowledge further about the properties of the signals. One of the most necessary new function is the two dimensional bifrequency spectral correlation function. It is the extension of the well-known power spectral density function for stationary signals.

The area of usage of this theory is relatively wide. Because many man-made signals are somehow involving underlying periodicities in the generating process, for example it has in communication, telemetry, radar and sonar systems, significant performance improvement is available with these signals exploiting the cyclical stationary nature. Some examples are like a decision as to the presence or absence of a random signal, about the number of random signals present, with a particular modulation type in a data set that also contains background noise and other modulated signals, secondly.

A classification of multiple received signals present in a noisy data set according to according to their modulation types, An estimate of a signal parameter, such as carrier phase, pulse timing, or direction of arrival, based on noise-and-interference-corrupted data set, a calculation between of an analogue signal or digital message being communicated by a signal over a channel corrupted by noise, interference and distortion.

A prediction of future value of a random signal, An estimate of the input-output relation of a linear or non-linear system based on measurement of the system’s response to random excitation, An estimate of the degree of causality between two data sets and finally.

An estimate of the parameters of a model from a data set Above these, signal processing also can find applications in climatology/meteorology, hydrology, medicine biology, oceanology, where cyclic features are origins from natural sources or for example, in acoustic noise analysis for rotating machinery, where the cyclic spectrum analysis supports location of noise sources.

But it has presence in the relatively smaller number of references from these fields indicates the difficulties of the detection or verification and exploitation of cyclostationarity of these signals. For the measurement of high order moments and cumulants are not introduced. These methods are comparatively analysed and obtained from computer simulation.

Read More : What Is Wide-Sense Cyclostationary Process

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