When comparing signals this way you could start averaging both signals amplitude this way:
A=[A1;A2;A3;A4];B=[B1;B2;B3];
mean(A)
=
1.25 3.50 -2.00 3.00
mean(B)
=
2.00 3.67 -2.33 5.33
then could check time average
mean(mean(A))
=
1.44
mean(mean(B))
=
2.17
that was 1st order statistical all-weather very useful mean, now 2nd order, variance:
var(mean(A))
ans =
6.18
var(mean(B))
ans =
10.85
the more samples the better, the longer the signals the more accurate the measurements
You may also want to see if the signals match a known statistical probability distributions that you may think would fit, for instance normal
fitdist(A,'Normal')
this in turn will give the probability distrition, then you can generate the pdf curve and measure how far the points of the signals are from the probability. The distance of the samples to an expected curve is an error measurement you may find useful
Error Vector Measurements are common in for instance DVB-T or for the case, any IQ signals.
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thanks in advance for time and attention
John BG
