zero lag when using xcorr on 2 near-similar, shifted timeseries
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Hi all,
I try to cross correlate two signals which are from 2 different instruments.
I use xcorr to find the lag between signal A (blue) and signal B (red). However, I keep getting 0 lag which should not be the case. As can be seen from the figure, the lag should be around ~ 110 where it gives a quit nice fit.
Attached are the signals A & B.
I'm using xcorr as the following:
[c,lags] = xcorr(A,B);
Thanks in advance for you help.
Kind regards,
Jos
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Respuestas (1)
dpb
el 22 de Sept. de 2023
Editada: dpb
el 26 de Sept. de 2023
load A, load B
whos
subplot(2,1,1)
plot(ADV_sampled)
hold on
plot(PIV_mag_test)
xlim([300 inf])
subplot(2,1,2)
plot(ADV_sampled)
hold on
plot(PIV_mag_test)
xlim([1 200])
legend('B','A','location','northwest')
The two are perfectly in phase over almost 90% of the total signal length; that the lag between the two is zero is what I would expect. From about sample 80 on, the peaks align almost perfectly.
Only the ramp up stage of B before any regular fluctuating behavior was achieved won't correlate highly, and there there simply isnt any significant similar behavior yet to even try to compare. I suppose B is somehow driven by A and you're trying to find where the two become phase locked, maybe?
The only area that appears to have a small phase shift is that A seems to lag B some in that initial startup section between 100 and 200+ samples, but other than within that small segment of the overall signal, there's simply so little lag that a lag shift by even one sample would appear to make the overall worse -- which is what xcorr() is telling you.
A subset section might show something...
2 comentarios
dpb
el 26 de Sept. de 2023
Editada: dpb
el 26 de Sept. de 2023
There simply isn't a phase lag to speak of anywhere there's a defined sinsoidal component so I don't think correlation is the tool that will help.
Making the best first match to the amplitude of the initial peak in A by subsequent peaks in B, might serve to give a way to align them; that might then need a bit of a lag adjustment after the initial guess.
With some creativity, it should be possible to use lsqnonlin to minimize the error between the two traces by adjusting the offset of B relative to A as the parameter to solve for -- the error function would have to account for the changing length for B as it is shifted in some manner; updating the indices to match lengths between the two as pieces are lopped off the front of B.
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