Autocorrelation of Similarity Matrix

The next step from creating similarity matrices is to generate an autocorrelation matrix. I use the matlab function normxcorr2() function for creating a normalized autocorrelation matrix.

Below is an autocorrelation plot of the similarity matrix generated by the unstabilized weighted scanline buffer. It is very difficult to see any pattern in this matrix.

Stabilizing the image affords a much more tractible autocorrelation of the similarity matrix as seen below.


Using a weight function improves the autocorrelation matrix even more:
What is interesting from these autocorrelation plots is that the peaks coincide with the periodic nature of the image sequence.

For this particular case, the distance between each peak correspond to the periodic rate of the radar. Shown below, the distance between each peak is 72 frames. This equates to 72 frames/29.97 fps = 2.4024 seconds/cycle which is roughly 25 rpm which is very accurate.


Cutler and Davis propose a mechanism for solving for the period by creating two different synthetic lattice matrices with the parameter d forming the distance between each lattice point. They run a match of these synthetic lattices with the autocorrelation matrix and choose d according to the lattice point that best matches. My first implementation of this method affords an estimate a distance of 68 instead of 72. There is an issue with implementation which I will look into later.

The main problem I see at this point is that even if I can measure the periodic rate of the radar, I'm still not detecting the position of the radar which is the main purpose of the effort. However measuring the periodicity will provide a means of queuing on pixels with this known periodic parameter.