On 09/02/11 23:08, Tijd Dingen wrote:
Henk wrote:
Wavecrest uses algorithms for this and their software gives a spectrum.
They also give some info on their site.
Henk, the paper you mentioned put me on the right track. Thanks. :)
Magnus wrote:
In "Jitter, Noise and Signal Integrity at High-Speed" by Mike Peng Li,
Prentice Hall, ISBN 0-13-242961-6 it is covered in pages 200-207.
It converts the phase jitter Phi(t) into the autocorrelation function
R_Phi(tau) (equation 7.40) and then the phase jitter PSD can be estimated
using the fourier transform (equation 7.41). The phase-noise can then be
calculated using the standard L(f) = 10*log10(PSD(f)/2). It's pretty
straight-forward.
While I don't have that book lying around here, going from the Wavecrest paper
Henk mentioned, I did find out that indeed the method is to calculate the
autocorrelation of the timeseries and then FFT this autocorrelation. The method
even has this easy to google name: Blackman-Tukey.
LeCroy has a paper with a short explanation that I found useful:
http://www.lecroy.com/files/WhitePapers/WP_TechBrief_Var_of_Time.pdf
The autocorrelation processing is O(N^2) while the DFT can be done in
O(N log N) when using FFT. As usual these can be implemented in reversed
order such that first the FFT is done to the phase jitter and auto-correlation
can be found using O(N) post-processing.
If it can be done in N log N that would be nice. :)
It's a reason why FFT has become so popular...
The phase-noise measuring ability of the SIA-3000 I have is not competing with
real phase-noise measuring sets. It doesn't say it can't be useful.
And what I am trying to do will probably not even compete with that SIA-3000.
It is more along the lines of... Hey I have these times stamps anyway. Now what
can I do with those besides calculate a frequency estimate of the frequency
under test?
Knock yourself out.. :)
Cheers,
Magnus
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