Bicoherence: Difference between revisions
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Revision as of 21:57, 24 September 2010
The following applies to the analysis of data or signals
The standard cross spectrum is the Fourier transform of the correlation
where the square brackets imply averaging over t. Similarly, the bispectrum is the Fourier transform of the bicorrelation
The signals Xi can either be different or identical. In the latter case, one speaks of the autocorrelation, autospectrum, auto-bicorrelation or auto-bispectrum.
Bispectrum
Denoting the Fourier transforms of the signals Xi(t) by
the bispectrum is defined as
where
Bicoherence
The bicoherence is obtained by averaging the bispectrum over statistically equivalent realizations, and normalizing the result:
The normalization is such that 0 ≤ b2 ≤ 1.
The bicoherence is symmetric under the symmetries (ω1,ω2) = (ω2,ω1) and (ω1,ω2) = (-ω1,-ω2), so that only one quarter of the plane (ω1,ω2) contains independent information. Additionally, for discretely sampled data all frequencies must be less than the Nyquist frequency: ω1,ω2,ω ≤ ωNyq. These restrictions define a polygonal subspace of the plane, which is how the bicoherence is usually represented.
The summed bicoherence is defined by
where N is the number of terms in the sum. Similarly, the total mean bicoherence is
where Ntot is the number of terms in the sum.
Interpretation
The bicoherence measures three-wave coupling and is only large when the phase between the wave at ω and the sum wave ω1+ω2 is nearly constant over a significant number of realizations.
Notes
- The bicoherence can of course be defined in wavenumber space instead of frequency space by applying the replacements x → t and ω → k.
- The bicoherence can be computed using the (continuous) wavelet transform instead of the Fourier transform, in order to improve statistics. [1]