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Wiener-Khinchin Theorem


The Wiener-Khinchin theorem relates the autocorrelation of a function to the absolute square of its Fourier transform. Recall the definition of the autocorrelation function C(t) of a function E(t),

 C(t)=int_(-infty)^inftyE^_(tau)E(t+tau)dtau.
(1)

Let E_nu=F_t[E(t)](nu) denote the Fourier transform of E(t). The inverse Fourier transform gives

 E(tau)=int_(-infty)^inftyE_nue^(2piinutau)dnu,
(2)

giving a complex conjugate of

 E^_(tau)=int_(-infty)^inftyE^__nue^(-2piinutau)dnu.
(3)

Plugging E^_(tau) and E(t+tau) into the autocorrelation function therefore gives

C(t)=int_(-infty)^infty[int_(-infty)^inftyE^__nue^(-2piinutau)dnu][int_(-infty)^inftyE_(nu^')e^(2piinu^'(t+tau))dnu^']dtau
(4)
=int_(-infty)^inftyint_(-infty)^inftyint_(-infty)^inftyE^__nuE_(nu^')e^(2piitau(nu^'-nu))e^(2piinu^'t)dtaudnudnu^'
(5)
=int_(-infty)^inftyint_(-infty)^inftyE^__nuE_(nu^')delta(nu^'-nu)e^(2piinu^'t)dnudnu^'
(6)
=int_(-infty)^inftyE^__nuE_nue^(2piinut)dnu
(7)
=int_(-infty)^infty|E_nu|^2e^(2piinut)dnu
(8)
=F_nu^(-1)[|E_nu|^2](t),
(9)

where the integral over tau giving the delta function is interpreted as a distribution. Thus the autocorrelation is the inverse Fourier transform of the absolute square of E_nu.

The Wiener-Khinchin theorem is a special case of the cross-correlation theorem with f=g.


See also

Autocorrelation, Cross-Correlation Theorem, Fourier Transform, Plancherel's Theorem, Power Spectrum

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Cite this as:

Weisstein, Eric W. "Wiener-Khinchin Theorem." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/Wiener-KhinchinTheorem.html

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