Fractional Fourier Transforms and Wigner Distribution Functions for Stationary and Non-Stationary Random Process

Abstract

In this paper, we discuss the relations among the random process, the Wigner distribution function, the ambiguity function, and the fractional Fourier transform (FRFT). We find many interesting properties. For example, if we do the FRFT for a stationary process, although the result in no longer stationary, the amplitude of its covariance function is still independent of time. Moreover, for the FRFT of a stationary random process, the ambiguity function will be a radiant line passing through (0, 0) and the Wigner distribution function will be invariant along a certain direction. We also define the fractional stationary random process and find that a non-stationary random process can be expressed a summation of fractional stationary random processes. The proposed theorems will be useful for filter design, noise synthesis and analysis, system modeling, and communication.