Clustering space debris detections

After we have the output of the range-velocity-acceleration matched filter bank, we still don't have a list of space debris detections. We need to use some machine learning algorithms to cluster out results. I tried K-means and some other techniques, but ended up with a physics based model, which compares expected range and velocity with measured range and velocity, relative to some trial point. This works remarkably well. I also needed to use the CLEAN algorithm to remove the effects of the range-doppler ambiguity (range smearing next to strong targets). 

Here's a labeled plot with before and after detections.

Matched filter output, time on y-axis, and range on the x-axis. Colored points mark detections.

The original matched filter output. It seems that I've missed one weak echo. 

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