I have two time dependent signal sources X & Y. Both can be modeled as having a linear combination of time dependent individual components and common components; so X(t)=a(t)+C(t)+noise, Y(t)=b(t)+C(t)+noise. For both signals I have multiple 'runs' of data, but only ~10 time samples per run. Which Method/techniques should I use to recover a(t) and b(t)? I have tried SVD, but it seems that a(t) and b(t) are similar enough to where their contribution gets 'mixed' in the U vector. The best results I have obtained have been through a High Pass filter because C(t) tends to move faster, but even that hasn't been as robust as I would like. I appreciate your input, thank you.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.