I tend to think of a matched filter as an "optimum denoiser" (in the mean squared error sense with white gaussian noise), but it's not an optimum detector in th

I tend to think of a matched filter as an "optimum denoiser" (in the mean squared error sense with white gaussian noise), but it's not an optimum detector in the general case, or an optimum "de-interferer". The case shown here should be more resilient to changes in signal amplitude and non-matching interference. Of course, because the kernel is actually learned, it can adapt to interference and try to be a "matched filter to what we want, plus anti-matched filter to interference" (i.e. give near-0 response to common interference signals). But SCS does seem like a more natural way to achieve a similarity metric and feature detection. (although this non-flexibility might work against it)