So I'm having some troubles with a signal that's been sampled but where the samples don't have consistent intervals. I've been looking into the NFFT (in python) as a starting point but I'm a complete noob with signal processing so I'm not really getting anything out of it.

So I have one array with the sample values and another with the corresponding timestamps. But I really have no idea how to get the spectrum out of it. I've done the first part as described here.

Sorry about the sparse question.

  • $\begingroup$ Hi! So if you have the nonuniform samples and the associated sampling time instants, then all you have to do is to feed them into the so called nonuniform FFT function. Please consult into Python help system, for learning how to do it in the software. $\endgroup$
    – Fat32
    Sep 4 '18 at 15:07
  • $\begingroup$ There are quite a few related questions on this website: take a look at dsp.stackexchange.com/q/22253/11256 dsp.stackexchange.com/q/25524/11256 for example. $\endgroup$
    – MBaz
    Sep 4 '18 at 15:19
  • $\begingroup$ Tnx for the comments, I'll take a look! $\endgroup$
    – RFmyD
    Sep 4 '18 at 15:36

The problem you have to solve is having a model for how your samples uniquely represent actually continuous input. For equidistant samples, the usual model is to stipulate (and then guarantee by analog filtering, oversampling, digital filtering, and decimation) that there are no signal components exceeding the Nyquist frequency.

What is your model going to be for the kind of non-uniform sampling you are working with? If the information you work with is not sufficient for that model, you are going to have non-uniquely interpretable results, namely aliasing.


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