I am well versed in cross-correlations, as my masters thesis heavily relied on them for music classification and beat detection. So this question focuses more on the signals that I am running an
xcorr on as opposed to the actual cross correlation process.
Here is a simplified version of my setup:
Suppose I have the original signal, which I time shift the signal (add a delay), and add noise/distort the signal. Then I run a cross-correlation on the two signals to find this time delay and get some value of R.
Is there some formulation to come up with a signal that will reduce ambiguity errors and optimize my value of R?
I know it will be some non-repeating, pseudo-random signal, but is there a formal formulation for this? Or should I be looking at other techniques?
Perhaps something like this would be perfect: http://tedxtalks.ted.com/video/TEDxMIAMI-Scott-Rickard-The-Wor. You see any issues with this?
I'm not talking about cross correlation processing techniques to improve the performance. I'm strictly talking about the shape of the waveform