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author | Daniele Nicolodi <nicolodi@science.unitn.it> |
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date | Mon, 05 Dec 2011 16:20:06 +0100 |
parents | f0afece42f48 |
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% FREQCORR Compute correlation between frequency bins %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % FREQCORR Compute correlation between frequency bins of a spectral % estimation given the data window % % CALL % % Gf = utils.math.freqCorr(w,eta,T) % % INPUT % % - w, window samples, Nx1 double, N must be the effective length of the % segments used for spectral estimation. E.g. For a periodogram N is equal % to the length of the data series. For a WOSA estimation N is the length % of each averaging segment. % - eta, frequency lag in Hz, 1x1 double % - T, sampling time in seconds, 1x1 double % % REFERENCES % % D. B. Percival and A. T. Walden, Spectral Analysis for Physical % Applications (Cambridge University Press, Cambridge, 1993) p 231. % % L Ferraioli 09-03-2011 % % $Id: freqCorr.m,v 1.1 2011/03/28 16:37:23 luigi Exp $ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function R = freqCorr(w,eta,T) Ns = numel(w); % willing to work with columns [nn,mm] = size(w); if nn<mm w = w.'; end % make suqre integrable a = sqrt(sum(w.^2)); w = w./a; t = 1:Ns; ww = w.*w; hh = exp(-1i.*2.*pi.*t.*T.*eta)*ww; R = abs(hh)^2; end