I have been reading about non-linear non-stationary signal analysis methods and it seems to do this type of analysis the go-to method is the Empirical Mode Decomposition (EMD), then Hilbert Transform (HT) to get instantaneous phase and frequency.

However I have been doing some analysis on a non-linear, non-stationary signal by splitting the time signal into intervals with a Hanning window and Fourier transforming each interval which I think might also be the idea behind the Short Time Fourier Transform (STFT), although I am not sure.

Can anyone tell me what is wrong with this method, and why the EMD / HT method is more advantageous? I have also seen wavelet analysis used in similar cases, is this something I should look into?

  • $\begingroup$ Don't know about non-linear as a generic problem but my old "hobby horse" is Wavelets for what this problem sounds like. $\endgroup$ – rrogers Jul 30 '19 at 20:31

Non-linearity and non-stationarity are non-properties. Without more details, they do not say much about the methods that may perform well, and moreover the choice depends a lot on what you really do: analysis, feature extraction, enhancement, filtering, component separation, restoration? What follows are typical sets of tools you could use:

All of the above can be combined somehow, like EMD at different scales in Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework. EMD has many uses and some known limitations at the same time.

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