Mercurial > hg > ltpda
diff m-toolbox/classes/+utils/@math/overlapCorr.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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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/m-toolbox/classes/+utils/@math/overlapCorr.m Wed Nov 23 19:22:13 2011 +0100 @@ -0,0 +1,63 @@ +% OVERLAPCORR Compute correlation introduced by segment overlapping +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% OVERLAPCORR Compute correlation introduced by segment overlapping in WOSA +% spectral estimations. It estimates the term contributing to the variance +% because of segment overlapping. +% +% CALL +% +% R = utils.math.overlapCorr(w,N,T) +% [R,n] = utils.math.overlapCorr(w,N,T) +% +% INPUT +% +% - w, window samples, Nsx1 double, Ns must be the effective length of the +% segments used for spectral estimation. E.g. For a periodogram Ns is equal +% to the length of the data series. For a WOSA estimation Ns is the length +% of each averaging segment. +% - N total length of data series, 1x1 double +% - navs, number of averages, 1x1 double +% +% REFERENCES +% +% D. B. Percival and A. T. Walden, Spectral Analysis for Physical +% Applications (Cambridge University Press, Cambridge, 1993) p 292. +% +% L Ferraioli 09-03-2011 +% +% $Id: overlapCorr.m,v 1.1 2011/03/28 16:37:23 luigi Exp $ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +function varargout = overlapCorr(w,N,navs) + + Ns = numel(w); + % willing to work with columns + [nn,mm] = size(w); + if nn<mm + w = w.'; + end + + % make suaqre integrable + a = sqrt(sum(w.^2)); + w = w./a; + + n = floor((N-Ns)/(navs-1)); + win = [w; zeros((navs-1)*n,1)]; + + R = 0; + for kk=1:navs-1 + ew = zeros(size(win)); + ew(kk*n+1:kk*n+Ns) = win(1:Ns); + + R = R + (navs-kk)*abs(win.'*ew)^2; + end + R = R./navs^2; + + if nargout == 1 + varargout{1} = R; + else + varargout{1} = R; + varargout{2} = n; + end + + +end \ No newline at end of file