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The partial with the maximum mean cross-correlation r ̄ p is taken as the reference.
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(3) We calculate the mean cross-correlation r ̄ p for each partial by averaging the pCAM−1 cross-correlation coefficients r i j obtained for each partial.
Instead of calculating mean cross-correlation values, we identified the minimum cross-correlation values associated with the presence of spurious pulses observed on one or a few sensors.
We then computed the mean cross-correlation, averaged across all cells and all the sliding windows.
The mean cross-correlation at each lag time was statistically tested using a one-sample t test (against the null hypothesis that the cross-correlation coefficient is 0).
The similarity index employed in this study is the image zero-mean, cross-correlation coefficient.
The lag that gives the maximum absolute cross-correlation is the most likely time lag.
A smaller set is obtained, including 4,749 sequences, among which we select 736 sequences having a maximum absolute value of the auto-correlation sidelobes equal to 12. From this subset, we further select 7 sequences with a maximum cross-correlation peak equal to 24, and 18 sequences with a maximum cross-correlation peak of 28.
These are further selected into a subset of 15 sequences, for which the maximum cross-correlation equals 24, and into a subset of 34 sequences, for which the maximum cross-correlation equals 28.
b The rotated waveforms with the maximum cross-correlation coefficient.
Then find the second maximum value of cross-correlation vector.
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