[time-nuts] ADEV slopes and measurement mode

Magnus Danielson magnus at rubidium.dyndns.org
Wed Apr 18 18:08:29 EDT 2018

```Hi Ole Petter,

On 04/16/2018 12:12 PM, Ole Petter Ronningen wrote:
> Hi, All
>
> This will be a bit long, and I apologize for it. Perhaps someone else also
> struggle with the same.
>
> One of the properties of the familiar ADEV-plots are the slopes - and how
> the slope identify the dominant noise type for the various portions of the
> plots. My understanding is that the slopes stem from "how the noise behaves
> under averaging" - White FM follows the usual white noise slope of
> 1/sqrt(N), or to put it another way; the standard deviation of a set of
> many averages of length N will fall as sqrt(N) when N increases. If the
> standard deviation of the whole non-averaged list is 1, the standard
> deviation of a set of 100-point averages will be 1/10. Other noise types
> does not follow the same 1/sqrt(N) law, hence will give rise to other
> slopes as the number of points averaged increases as we look further to the
> right of the plot.
>
> If I have already droppped the ball, I'd appreciate a correction..

Hrhrm!

It's not directly averaging but kind of. Ehm. Let me see how to explain
this...

When you measure frequency, one way or another, two time-stamps is
formed. The distance between those two time-stamps will be the
observation time tau. As you count how many cycles, often called events,
that occurred over that time, you can calculate the frequency as
events/time. This is the basis of all traditional counters, which we
these days call Pi-counters, for reasons I will explain separately.

Now, we could be doing this measurement as N sequential back-to-back
measurements, where the stop event becomes the start event of the next
measurmeent. As we sum these up the phase of the stop and the start of
the next cancels and the full sum will become that of the first
start-event and last stop-event. Regardless how we divide it up or not,
it will end up being the same measurement. The averaging thus kind of
cancels out and interestingly does not do anything.

Now, as we attempt to establish the statistical stability of this value
using the normal standard variance and standard deviation methods, also
known as Root Mean Square (RMS) algorithm, we have a bit of a problem,
because the noises of an oscillator is non-convergent. So, an
alternative method to handle that was presented in the 1966 Feb article
by that David Allan. It still provide variance and deviation measures,
but without being caught by the convergence problems.

The ADEV for a certain observation interval is thus an equivalent to
standard deviation measure, to explain how good stability there is for
the measure at that observation interval, regardless of how we divided
the measurement up to form the frequency estimations, as long as they
form a continuous measurement, thus without dead-time.

The slopes stems from how the 1/f^n power distributions of noises gets
inverse Fourier transformed into time-domain, which is the basis for
David Allans article, he uses that out of the M.J.Lighthill "An
introduction to Fourier analysis and generalised functions" and adapts
to the power distributions noises. Because different 1/f^n noises is
dominant in different parts of the spectrum, their slopes in the time
domain will also show the same dominant features and thus be the limit
of precision in our measurement.

Now, if there happens to be gaps in the data as gathered, the dead-time
would cause a bias in the estimated stability, but that bias can be
predicted and thus compensated for, and was passed to a separate bias
function that was also modeled in the same article. That way, the bias
of the counters they had at the time could be overcome to result in
comparable measures.

The obsticle of the averaging over N samples that prohibited the use of
the normal RMS function could be separated out into another bias
function that also was modeled in the article. This way a 5-point
stability measure and a 3-point stability measure could be compared, by
converting it into the 2-point stability measure. The 2-point variance
later became coined Allan's variance and later Allan variance. That
article forged the ring to control the variances.

I can explain more about how the averaging does not give you more help,
but the bias function is good enough. I've done simulations to prove
this to myself and it is really amazing to see it misbehave as soon as
there is a slope there. Classical statistics is out the window.

> Now onto where I confuse myself (assuming I havent already): measuring
> phase vs measuring frequency, and white FM versus white PM. Specifically,
> using a frequency counter to either measure the frequency directly, or
> setting up a time interval measurement to measure phase.

Actually, you tap the data from your counter in two different ways,
either just to get time-stamps one way or another, or get two
time-stamps that has been post-processed into a frequency estimate.

A good crash-coarse on how frequency counters do their dirty work can be
had by reading the HP5372A programmers manual. It actually shows how to
use the raw data digital format and do all the calculations yourself.

A more gentle approach can be had by reading the HP Application Note 200
series.

> to the measurements, my initial reasoning was as follows:
> * When measuring frequency, the quantization noise is added to the
> frequency estimate. This, when converted to phase in the adev calculations,
> will result in random walk in the phase plot - and hence a slope of -0.5.
> "Two over, one down".
> * When measuring the phase directly in a time interval measurement, the
> noise is added to the phase record, and will not result in random walk in
> phase - and hence a slope of -1. "One over, one down"

The way that classical counters do their business, and here I mean any
counters doing Pi-counters, for many purposes the time and frequency
measurements is essentially the same things. The only real difference is
if you time-stamp phase as a you do in a frequency measurement, and thus
compare the reference clock and the measurement channel, or if you
measure the time-difference between two channels using the reference
channel as a "transfer-clock".

> This is in fact not what happens - or kinda, it depends, see below. As
> someone on the list helpfully pointed out; as the gate time of your
> frequency measurements increase, the noise stays the same. So the slope is
> -1. This makes perfect sense when measuring phase, and is consistent with
> my reasoning above - the way to "average" a phase record is simply to drop
> the intermediate datapoints.
>
> My confusion concerns measuring frequency. I can not see why the slope is
> -1, and I am confused for two reasons:
>
> 1. The gate time as such is not increasing - we are averaging multiple
> frequency readings, all of them has noise in them which, in my mind, when
> converted into phase should result in random walk in phase and follow the
> -.5 slope.

See my explanation above. The averaging you do fools you to believe you
got more data in then you did.

phase - phase + phase - phase + phase - phase
= phase - phase.

That's all there is to it. What you gain by the longer measurement is
that you get a longer time for the single-shot resolution of phase
and phase to affect the precision, this is a systematic effect which
is trivial to understand. We also get in the more subtle point of
stability slope and we get a reading from where there is less effective
noise because the length of the reading got longer. It can get so long
that we start to loose again.

ADEV and friends is greatly misunderstood, it's a way to get stability
measures for the type of readout you do with your counters. This was at
the time when for this span there was no other useful method than
counters. Much of the ADEV limits and motivation comes from the limited
instruments of the times. Once the tool was there and the strength of
various noises could be determined, it became handy tool.

> 2. Trying to get to grips with this, I also did a quick experiment - which
> only increased my confusion:
>
> I set up my 53230a to measure a BVA-8600 against my AHM, in three modes,
> tau 1 second, each measurement lasting one hour:
> 1. Blue trace: Time interval mode, the slope on the left falls with a slope
> of -1, as expected.
> 2. Pink trace: Frequency mode, "gap free"; the slope on the left also falls
> with a slope of -1, somewhat confusing given my reasoning about random walk
> in phase, but could maybe make sense somehow.

Notice how these two have the same shape up to about 200 s, at which
time they start to deviate somewhat from each other. What you should
know that the confidence interval for the ADEV starts to open up really
much at 200 s, and as you sit down and see TimeLab collect the data in
real time, you can see that the upper end flaps around up and down, just
illustrating that the noise process is yanking it around and you don't
know where it will end up. Do the same measurement again and they could
look differently, that's the meaning of confidence interval, it just
gives you a rough indication that the noise process makes the true value
to be with a certain certainty within those limits, but not where. As it
opens up, the true value can be in an increasingly wide range. This is
due to lack of degrees of freedom, and was DF increases, the confidence
interval becomes tighter. ADEV is thus following a chi-square statistics.

There is a small offset between the blue and pink trace. It would be
interesting to find out where that offset comes from, but they should
trace the same until confidence inverval blows up.

> 3. Green trace: Frequency mode, "RECiprocal" - *not* gap free. The slope on
> the left falls with a slope of -0.5. Very confusing, given the result in
> step 2. Random walk is evident in the phase plot.

Hmm, strange.

> Since the 53230A has some "peculiarities", I am not ruling out the counter
> as the source of the difference in slopes for gap free vs not gap free
> frequency measurement - although I can see how gaps in frequency leads to
> random walk in the phase record and a slope of -0.5 - I just cant see how a
> gap free frequency record (where every frequency estimate contains an
> error) does *not* result in RW in phase.. :)

Yeah. It should not do that.

> So my questions are:
> 1. Does gaps in the frequency record always lead to random walk in phase
> and a slope of -0.5, is this a "known truth" that I missed? Or is this
> likely another artifact of the 53230A?

Gaps would give a bias, and that bias function is known.
Check if that could be what you seek for.

> 2. Is my understanding of how the slopes arise, they show how the noise
> types "behave under averaging", correct?

No, not really.

> 3. Depending on the answer to 1; What am I missing in my understanding of
> white PM vs white FM - why does gap free frequency measurement not lead to
> a slope of -0.5?

Because the underlying power distribution slopes does not inverse
fourier convert to time like that. However, other mecanisms of gapped
data interacts. I would recommend a careful reading of David Allan's
paper from Feb 1966. It takes a few readings to figure out what's going on.

> Thanks for any and all insight!
> Ole

I hope I have contributed some to the understanding.

> ---------
>  Thinking about how frequency measurements are made, it kinda maybe
> makes sense that the slope is -1; the zerocrossings actually *counted* by
> the frequency counter is not subject to noise, it is a nice integer. It is
> only the start and stop interpolated cycles that are subject to
> quantization noise, and as more and more frequency estimates are averaged,
> the "portion" of the frequency estimate that is subject to the noise
> decreases linearly. I suppose.

Well, turns out that the 1/tau slope is not well researched at all. I
have however done research on it, and well, the way that noise and
quantization interacts is... uhm... interesting. :)
The non-linearity it represents does interesting things. I have promised
to expand that article to a more readable one.

You can however understand how the time quantization part of it works,
and the single-shot resolution of the start and stop methods is really
the key there.

Now, I did talk about Pi-counters. A Pi-counter is a counter which
provides equal weighing to frequency over the measurement period, that
which is about tau long. The shape of the weighing function looks like a
greek Pi-sign. As you integrate this, you take the phase at stop minus
the phase of start, that just how the math works and is fully
equivalent, and very practical as the frequency is not directly
observable but the relative phase is, so we work with that.

Now, what J.J.Snyder presented in a paper 1980 and then 1981 was an
improved measurement method used in laser technology that improved the
precision. It does this by averaging over the observation interval. This
then inspired the modified Allan deviation MDEV which solved the problem
of separating white and flicker phase noise. For white noise, the slope
now becomes tau^-1.5 rather than tau^-1. This thus pushes the noise down
and reveals the other noises more quickly, which is an improved
frequency measure. The separation of noise-types is helpful for
analysis. This was also used in the HP53131/132A counters, but also K+K
counters etc. and has been know as Delta-counters, because of the shape
of the frequency weighing looks like the greek Delta-sign. Frequency
measures done with such a counter will have a deviation of the MDEV and
not the ADEV. As you measure with such a frequency estimator, you need
to treat the data correct to extend it properly to ADEV or MDEV. Also,
many counters output measurements that is interleaved, and if you do not
treat it as interleaved you get the hockey-puck response at low tau,
which wears of at higher taus at which time you have the ADEV as if you
used raw time-samples and all you got was a bit of useless data at the
low-tau end.

Some counters, including the Philips Industrier/Fluke/Pendulum
PM-6681/CNT-81 and PM-6690/CNT-90 uses a linear regression type of
processing to get even better data. This was presented in papers and
app-notes by Staffan Johnsson of Philips/Fluke/Pendulum. He also showed
that you could do ADEV using that. Now, Enrico Rubiola got curious about
that, and looked into it and realized that the linear regression would
form a parabolic weighing of the frequency samples, and that the
but a parabolic deviation PDEV. The trouble with the linear regression
scheme is that compared to the others, you cannot decimate data, where
as for the others you can. This meant that for proper PDEV calculations
you could not use the output of a Omega-counter, again to reflect the
shape of the weighing of frequency, but you needed to access the data
directly. However, I later showed that the equivalent least-square
problem could be decimated in efficient form and do multi-tau processing
memory and CPU efficient in arbitrary hierarchial form.

The filtering mechanisms of Pi, Delta and Omega shapes is really
filtering mechanisms that surpresses noise, which makes frequency
measures more precise as you follow the development chain, and their
deviations is the ADEV, MDEV and PDEV respectively. As you then want to
estimate the underlying noise-powers of the various 1/f^n power
distributions, you need to use the right form to correctly judge it. If
biases functions to correct the data to understand it in ADEV equivalent
plots, but that would not be valid of the Delta or Omega prediciton of
frequency.

Similarly can phase be estimated, but again the weighing of frequency,
or for that matter phase, over the observation time will render
different precision values. The time deviation TDEV is based on a
rescaled MDEV, so that is valid only for measures using a Delta-shaped
frequency weighing. The lack of time deviation of Pi and Omega shapes
remains an annoying detail, but TDEV was not meant for that purpose.

OK, so this is the first lesson of "ADEV greatly missunderstood". :)

Most people that say they understand ADEV and friends actually don't
understand it.

Cheers,
Magnus

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