Biorthogonal decomposition: Difference between revisions

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e.g., travelling waves have a structure such as ''cos(kx-ωt)''; however, most propagating waves can still be recognised clearly by their distinct footprint in the biorthogonal modes (provided there are not too many): a travelling wave will produce a pair of modes with similar amplitude and a 90° phase difference.
e.g., travelling waves have a structure such as ''cos(kx-ωt)''; however, most propagating waves can still be recognised clearly by their distinct footprint in the biorthogonal modes (provided there are not too many): a travelling wave will produce a pair of modes with similar amplitude and a 90° phase difference.


== Relation with signal correlation ==
== Relation with signal covariance ==


The correlation between signals ''j<sub>1</sub>'' and ''j<sub>2</sub>'' is defined as:
Assuming the signals ''j<sub>1</sub>'' and ''j<sub>2</sub>'' have zero mean (their temporal average is zero), their [[:Wikipedia:Covariance|covariance]] is defined as:


:<math>C(j_1,j_2) = \sum_i {Y(i,j_1)Y(i,j_2)},\!</math>
:<math>C(j_1,j_2) = \sum_i {Y(i,j_1)Y(i,j_2)},\!</math>
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:<math>C(j_1,j_2) = \sum_k {\lambda_k^2 \phi_k(j_1)\phi_k(j_2)}</math>
:<math>C(j_1,j_2) = \sum_k {\lambda_k^2 \phi_k(j_1)\phi_k(j_2)}</math>


The technique is therefore ideally suited to perform cross correlation analyses of multipoint measurements.
The technique is therefore ideally suited to perform cross covariance analyses of multipoint measurements.


By multiplying this expression for the correlation matrix ''C'' with the vector &phi;<sub>k</sub> it is easy to show that the topos &phi;<sub>k</sub> are the eigenvectors of the correlation matrix ''C'', and &lambda;<sub>k</sub><sup>2</sup> the corresponding eigenvalues.
By multiplying this expression for the covariance matrix ''C'' with the vector &phi;<sub>k</sub> it is easy to show that the topos &phi;<sub>k</sub> are the eigenvectors of the covariance matrix ''C'', and &lambda;<sub>k</sub><sup>2</sup> the corresponding eigenvalues.


== See also ==
== See also ==