Wigner distribution function

The Wigner distribution function (WDF) is used in signal processing as a transform in time-frequency analysis. The Wigner distribution function (WDF) is used in signal processing as a transform in time-frequency analysis. The WDF was first proposed in physics to account for quantum corrections to classical statistical mechanics in 1932 by Eugene Wigner, and it is of importance in quantum mechanics in phase space (see, by way of comparison: Wigner quasi-probability distribution, also called the Wigner function or the Wigner–Ville distribution). Given the shared algebraic structure between position-momentum and time-frequency conjugate pairs, it also usefully serves in signal processing, as a transform in time-frequency analysis, the subject of this article. Compared to a short-time Fourier transform, such as the Gabor transform, the Wigner distribution function can furnish higher clarity in some cases. There are several different definitions for the Wigner distribution function. The definition given here is specific to time-frequency analysis. Given the time series x [ t ] {displaystyle x} , its non-stationary autocorrelation function is given by where ⟨ ⋯ ⟩ {displaystyle langle cdots angle } denotes the average over all possible realizations of the process and μ ( t ) {displaystyle mu (t)} is the mean, which may or may not be a function of time. The Wigner function W x ( t , f ) {displaystyle W_{x}(t,f)} is then given by first expressing the autocorrelation function in terms of the average time t = ( t 1 + t 2 ) / 2 {displaystyle t=(t_{1}+t_{2})/2} and time lag τ = t 1 − t 2 {displaystyle au =t_{1}-t_{2}} , and then Fourier transforming the lag.