[time-nuts] Calculate spectral content from a series of zerocrossing time stamps?
tijddingen at yahoo.com
Tue Feb 8 05:15:31 UTC 2011
Didn't know that one yet, so will check it out. Thank you for the suggestion! :)
----- Original Message ----
From: ehydra <ehydra at arcor.de>
To: Discussion of precise time and frequency measurement <time-nuts at febo.com>
Sent: Tue, February 8, 2011 5:24:39 AM
Subject: Re: [time-nuts] Calculate spectral content from a series of
zerocrossing time stamps?
SpectrumLab uses the Goertzel-Algorithm to trim the time reference to the
millisecond range. Maybe that is comparable and as algoritm transverable?
J. L. Trantham schrieb:
> DFT? Direct Fourier Transform?
> -----Original Message-----
> From: time-nuts-bounces at febo.com [mailto:time-nuts-bounces at febo.com] On
> Behalf Of Tijd Dingen
> Sent: Monday, February 07, 2011 9:02 PM
> To: time-nuts at febo.com
> Subject: [time-nuts] Calculate spectral content from a series of
> zerocrossing time stamps?
> 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
> 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?
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