[time-nuts] Homebrew frequency counter, need help

Magnus Danielson magnus at rubidium.dyndns.org
Sun Dec 14 12:35:01 EST 2014


On 12/12/2014 08:01 PM, Tom Van Baak wrote:
> Hi Li,
> What you're doing is the same "trick" the Pendulum CNT-91 uses, as well as modern Agilent frequency counters, and even my own picPET.
> The good news is that for frequency measurement all those many samples and the sqrt(N) advantage allow you to measure the frequency far more accurately than with traditional methods. That's why a hp 53132A can rightly advertise "12 digits/second".
> The bad news is that what you gain in frequency resolution you lose in temporal resolution. This is why in spite of having 1 ps/s frequency specs, the counter has 150 ps single-shot time resolution. What most time interval counters then do is average in order to gain precision. This works fine unless what you're trying to measure is not time, or even frequency, but frequency stability (modulation domain). In that case averaging may remove the very thing you are trying to measure.
> As for ADEV, all you need is the raw phase data, even at 9000 points per second (USB is fast enough), and let TimeLab take care of the rest. It will properly scale, decimate, and filter the data to produce correct ADEV plots, from your minimum tau0 of 0.000111 s out any tau you want. You will quickly see the noise floor of the counter this way.
> Some papers to read:
> Continuous time stamping
> http://www.spectracomcorp.com/Desktopmodules/Bring2Mind/DMX/Download.aspx?EntryId=450

This is a good overview. Staffan got some minor factoids wrong, but who 
cares? It brings out the basic idea of history.

That ses of links is a good read. The HP Application note 200 series is 
also a good read for counters in general.

Do notice Staffan's heads-up that you need to measure on stable signals, 
as severer frequency drift will cause values to be, well a bit 
interesting. The reason is that the linear regression used is for a 
linear model and not quadratic model. Jim Barnes did an interesting 
analysis, and it turns out that regression measurements can be a bad 
solution for drift analysis.

The lesson that Staffan and Jim gives us is that just trying to apply 
your favorit magic without understanding side-consequences of systematic 
effects, may not give the effects you think.


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