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 12) = (ω21) and 12) = (-ω1,-ω2), so that only one quarter of the plane 12) contains independent information. Additionally, for discretely sampled data all frequencies must be less than the Nyquist frequency: ω12,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 ω12 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]

References