I have a signal S, which needs to be split into two components Sx and Sy.
And I have a signal X, which is a reference signal corresponding to Sx.
I need to perform this split of S and check that resulting Sy is ~Y and Sx ~X (I can use X in the process of filtering\separation, but not Y).
This sounds like a typical denoising task, that can be accomplished with LMS\RLS filters, but in my case signals X and Y are correlated and their BW are overlapping.
They are also non-stationary:
- sometimes X amplitude can decrease to almost 0, then Sx is also 0 and Sy should be estimated as simply S.
- sometimes both X and Y are 0 for a short period of time.
- most of the time X and Y are approx sinusoidal signals with similar central frequencies, one might be slightly shifted w.r.t another.
I tried regular LMS\RLS approaches - assume Sx is noise -> S = Sy + noise, but due to crosscorrelaton between S,X and Y the algorithms best guess is S = Sy.
1) How would you try to solve this? 2) What if we can use Y as well, at least for the beginning? Would it make it simpler?
3) More specific question. Now I have S,X,Y amplitudes in arbitrary units (adc counts). Is it better to scale them? Otherwise, I assume, choice of Sx and Sy will be dependent on amplitude ratios of the signals, or not?