Reply by Miso Soup Addict●December 14, 20042004-12-14
Errr, find the correlation?
If I remember correctly, remove the DC, multiply and sum the terms of
series A and B..
ie sum = a1*b1 + a2+b2 +.....
The sum will be proportional to the correlation between the two
signals.
Or if they are very similar, finding the mean squared error between
the two signals
ie sum= (a1-b1)^2 + (a2-b2)^2 .
Failing that, neural networks that can be trained to accept or reject?
The magnetic signals from cars - these cars are moving - have you
factored in the different car velocities ? Ie the same car might have
different signatures at different speeds etc.
Reply by Miso Soup Addict●December 14, 20042004-12-14
Errr, find the correlation?
If I remember correctly, remove the DC, multiply and sum the terms of
series A and B..
ie sum = a1*b1 + a2+b2 +.....
The sum will be proportional to the correlation between the two
signals.
Or if they are very similar, finding the mean squared error between
the two signals
ie sum= (a1-b1)^2 + (a2-b2)^2 .
Failing that, neural networks that can be trained to accept or reject?
The magnetic signals from cars - are these cars are moving - have you
factored in the different car velocities ? Ie the same car might have
different signatures at different speeds etc.
Reply by Shafik●December 13, 20042004-12-13
Hello wizards,
Is there a "best" known way for comparing two finite-same-sized
digital signals? I want a good way to measure the degree of similarity
of the *shape* of the two signals. This is for comparing magnetic
signatures of vehicles.
Is the best way just to do a scaled cross correlation between the two
signals?
--Shafik