compare multiple cross sections over time hypack

And via the correlation's link to the Discrete Fourier Transform (DFT) that enables us to carry it out in the frequency domain we can start to see the equivalences between the given statements.Ī more informative division would be between linear 'metrics' and non-linear metrics. But each one of the similarities mentioned, is equivalent to the other at least when we are talking about signals originating from linear systems.Ĭross correlation provides very good results especially if you are trying to figure out if a signal is noise or not and a very good application of that is in the rake receiver. Maybe they do, depending on the application. best results come from a weighted ensemble of techniques. Can anyone point me to a more thorough discussion of these techniques, preferably with some python code or in lieu of that, some code in R?

Similarity in energy (or power if different lengths)īut this is a fairly high-level outline.

This will show if the signals share similar spectral shapes.

Similarity in frequency domain (with shift*): Multiply the two signals and take FFT.

Similarity in frequency domain (static**): Take FFT of each signal, multiply, and sum.

Similarity in time domain (with shift*): Take FFT of each signal, multiply, and IFFT.

Similarity in time domain (static): Multiply in place and sum.This very informative post suggests that best results come from a weighted ensemble of techniques, including: We're exploring machine learning techniques, but we also want tackle this as a signal processing problem and see where we get the best results. I have multiple (between 2 and 100) signals and need to determine when a significant number diverge from the rest.

Compare multiple cross sections over time hypack