Mercurial > hg > ltpda
comparison m-toolbox/classes/+utils/@math/freqCorr.m @ 0:f0afece42f48
Import.
| author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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| date | Wed, 23 Nov 2011 19:22:13 +0100 |
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| -1:000000000000 | 0:f0afece42f48 |
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| 1 % FREQCORR Compute correlation between frequency bins | |
| 2 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| 3 % FREQCORR Compute correlation between frequency bins of a spectral | |
| 4 % estimation given the data window | |
| 5 % | |
| 6 % CALL | |
| 7 % | |
| 8 % Gf = utils.math.freqCorr(w,eta,T) | |
| 9 % | |
| 10 % INPUT | |
| 11 % | |
| 12 % - w, window samples, Nx1 double, N must be the effective length of the | |
| 13 % segments used for spectral estimation. E.g. For a periodogram N is equal | |
| 14 % to the length of the data series. For a WOSA estimation N is the length | |
| 15 % of each averaging segment. | |
| 16 % - eta, frequency lag in Hz, 1x1 double | |
| 17 % - T, sampling time in seconds, 1x1 double | |
| 18 % | |
| 19 % REFERENCES | |
| 20 % | |
| 21 % D. B. Percival and A. T. Walden, Spectral Analysis for Physical | |
| 22 % Applications (Cambridge University Press, Cambridge, 1993) p 231. | |
| 23 % | |
| 24 % L Ferraioli 09-03-2011 | |
| 25 % | |
| 26 % $Id: freqCorr.m,v 1.1 2011/03/28 16:37:23 luigi Exp $ | |
| 27 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | |
| 28 function R = freqCorr(w,eta,T) | |
| 29 | |
| 30 Ns = numel(w); | |
| 31 % willing to work with columns | |
| 32 [nn,mm] = size(w); | |
| 33 if nn<mm | |
| 34 w = w.'; | |
| 35 end | |
| 36 % make suqre integrable | |
| 37 a = sqrt(sum(w.^2)); | |
| 38 w = w./a; | |
| 39 | |
| 40 t = 1:Ns; | |
| 41 | |
| 42 ww = w.*w; | |
| 43 hh = exp(-1i.*2.*pi.*t.*T.*eta)*ww; | |
| 44 R = abs(hh)^2; | |
| 45 | |
| 46 | |
| 47 end |