Consider the following scenario. We have a signal source of about 10 kHz,
with unknown phase noise. Let's for simplicity's sake assume for now that
the phase noise is large enough that it will be detectable by the following
approach.
We measure every zero crossing with lets say 1 ns accuracy. So we have a
signal with a nominal period of 100 us, and we can measure every zero crossing
to within 1 ns. This gives you ~ 10,000 data points every second.
Now how does one efficiently calculate the spectral content based on these
10,0000 zero crossings? The end result would be the spectral density, centered
around that nominal 10 kHz frequency.
>From what I could find so far, one method to go about this is use a
Lomb/Scargle Periodogram. And specifically the method by Press & Rybicki that
extirpolates the unevenly timed samples to an regular timed mesh, after which
a regular DFT is done.
This is a nice enough approach, but you pay a computational price for the fact
that this algorithm is able to handle more generic inputs than is needed in
this particular case. So possibly there is a more efficient method, only
which one?
Any suggestions for methods/papers/etc are appreciated. :-)
thanks!
Fred
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