Hi Bob,
On 12/14/2014 06:00 PM, Bob Camp wrote:
Hi
On Dec 14, 2014, at 11:36 AM, Magnus Danielson <[email protected]>
wrote:
On 12/12/2014 06:15 PM, Bob Camp wrote:
Hi
On Dec 12, 2014, at 11:44 AM, Li Ang <[email protected]> wrote:
Yes, you are right. 5650_5650 is sig=ref case. prs10_5650 is sig=prs10 and
ref=5650 case.
In the “both same (5650 / 5650) case” your linear regression filtering is
faking you out a bit. The SR620 counter has exactly this same issue. That’s
probably for the same reason. It’s a fine test to see if you have various
problems under control. It’s not a perfect way to estimate the number of digits
you will get on a real measurement. Using two independent sources is a better
way to do that.
When you have two identical signals, the TDC noise is the main issue. All the
edges are arriving in the same relation to each other (same timing). The linear
regression is (obviously) good at suppressing the sort of noise the TDC has.
With two independent signals the noise is more complex. The edges arrive at
various times relative to each other. More things contribute to the total
noise. The linear regression is having a harder time suppressing that sort of
noise. In some cases (as you observed) the linear regression is actually making
things worse.
If Magnus was here, he would be tossing empty beer bottles at me and saying —
see Bob, sqrt(N) doesn’t always work ….
Indeed, except I would not be tossing empty beer bottles at you, I might
jokingly attempt do, but never actually throw it. One has to realize that the
quantization noise of the TIC may seem to process as if it where white phase
noise, but it isn't random noise, it is a systematic noise and if you fool
around with the systematics is may work for you or against you.
I do consider to pass another bottle of good beer to Bob for good behavior. :)
…. as long as we don’t start tossing kegs at each other, I consider myself
lucky :)
Maybe empty, the beer will be so hard to tap if you throw full kegs,
even if full kegs have better impact to get the point through. :)
The filtering process used does need to be adapted to the noise of the total
system.
It's one of the forgotten parameters, and I've even seen good Sam Stein stand up and say
"we used to do this wrong" on the same point, you need to publish and consider
the bandwidth of your processing, as it *will* affect the ADEV plot (but only MDEV and
TDEV somewhat).
Since I really want to reduce the noise, what is the best test set you
suggest? All the frequency source I have: FE5650 Rb , PRS10 Rb , MV89a*2
OCXO,
If the MV89’s are in good working condition, they are the best thing to
compare.The have the best ADEV of the group you have available I would check
them for output level and stability before I trusted them. There are a lot of
defective parts on the market. People get some, sort them and sell the bad
ones. The bad ones just keep getting re-sold again and again … My guess is that
they were good parts at one time and they got damaged when pulled off boards.
If you use them, keep them on power at all times. Any OCXO will do better if
you run it that way.
In order to test if systematics is messing badly with you, measure the ADEV of
the oscillator as it is steered (and stabilized) to a number of different
frequencies. For larger offsets to the counter reference, multiple beatings
occurs within the regression interval. You want that number to be an even
number of beats, or the beat count to be so large that the phase of the last
beat does not care. Linear regression helps out, as it weighs out the outermost
measures compared to the central one, making the beating at the beginning and
end not care as much.
These are *systematic* noise effects, and as you play around with systematics
and processing, you might have the systematics works for or against you, but at
the same time, the random noise you try to measure will suffer the processing
filtering, and you need to recall that. If you balance these properly, you can
make good and correct measurements, it's just that few do.
Oh, and only use ADEV, MDEV and TDEV to estimate random noises, system noises
as they show up there should be estimated separately and removed from the
random noise estimates. They have *way* different behaviors.
… and this is where it gets complicated. I would toss in the Hadamard deviation
into that mix as well.
The Hadamard deviation is a great tool as it is not sensitive to linear
frequency drift as Allan deviation is. This would help to remove the
systematic effect, just as a quadratic curve-fitting of the raw-data and
ADEV of the residual.
Modified Hadamard deviation (MHDEV) is a good replacement for MDEV, with
the same properties for drift. Similarly will Time Hadarmard Deviation
(THDEV) replace TDEV. However, for longer taus you want better
processing, so therefore you want to consider the TOTAL set of
deviations, such that confidence intervals is better.
If I had to only use three, I would include it with modified ADEV (MDEV) and
TDEV. All three are available in TimeLab with the click of a button. If you
start getting lots of data (9,000 points per second) I would toss in a
frequency domain (FFT) analysis as well. FFT on phase data is not (as far as I
know) a feature of TimeLab.
FFT on phase-data is only available in TimeLab when doing phase-noise
measurements. FFT is the way to analyse systematic noise rather than
random noise where ADEV and friends is being used. You need to separate
them, and the ADEV plot is not good for both.
There is a set of FFT based ADEV-style measures, which uses FFT,
filtering of the various ADEV styles. There is a nice set of articles
covering that approach, and actually the only style of ADEV processing
that I haven't yet implemented, even if I have done most others.
To start with, on all of these measures, you are looking for bumps and spikes.
They are telling you that something is wrong. If you flip over to the phase
plot in TimeLab, spikes and abrupt steps in it also are telling you the same
sort of thing. Exactly what this or that bump is telling you may not be obvious
at first. Posting plots to the list is a great way to get things sorted out.
Bumps, spikes and slopes... ADEV isn't the only tool one should be
using, FFT might be much better for systematic noises.
In the end of the day, there is an overbeleife in ADEV both as a scale
as well as a processing tool, to analyze deviations, without considering
the separation of various systmeatic effect and systematic noises, while
ADEV and friends is there to analyze random noise types, it does not
handle systematics good. Seems like we have to kill ADEV as the
universal measure. Ah well.
Cheers,
Magnus - considering what beer will be best to start the evening with
That means it’s 5 o’clock somewhere in the world …hmmm …. choice of beers …
It’s winter over here, so the dark stuff is slowly taking over the inventory. I
have a nasty suspicion that it’s winter in Sweden as well :). Probably
something with stout in it’s name ….
Hibernation Ale from Great Divide Brewing in Denver, Colorado, USA was
the choice for the evening. Good beer for handling the winter.
Cheers,
Magnus
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