Re: [time-nuts] Modified Allan Deviation and counter averaging
Poul-Henning, On 08/03/2015 01:07 AM, Poul-Henning Kamp wrote: In message 55bdb002.8060...@rubidium.dyndns.org, Magnus Danielson writes: For true white PM *random* noise you can move your phase samples around, but you gain nothing by bursting them. I gain nothing mathematically, but in practical terms it would be a lot more manageable to record an average of 1000 measurements once per second, than 1000 measurements every second. Yes, averaging them in blocks and only send the block result is indeed a good thing, as long as we can establish the behavior and avoid or remove any biases introduced. Bursting them in itself does not give much gain, as the processing needs to be done anyway and even rate works just as well. A benefit of a small burstiness is that you can work on beat-notes not being multiple of the tau0 you want, say 1 s. As in any processing, cycle-unwrapping needs to be done, as it would waste the benefit. For random noise, the effect of the burst or indeed aggregate into blocks of samples is just the same as doing overlapping processing as was done for ADEV in the early 70thies as a first step towards better confidence intervals. For white noise, there is no correlation between any samples, so you can sample them at random. However, for ADEV the point is to analyze this for a particular observation interval, so for each measure being squared, the observation interval needs to be respected. For the colored noises, there is a correlation between the samples, and it is the correlation of the observation interval that main filtering mechanism of the ADEV. However, since the underlying source is noise, you can use any set of phase-tripplets to add to the accumulated variance. The burst or block average, provides such a overlapping processing mechanism. However, for systematic noise such as the counter's first order time quantization (thus ignoring any fine-grained variations) will interact in different ways with burst-sampling depending on the burst length. This is the part we should look at to see how we best achieve a reduction of that noise in order to quicker reach the actual signal and reference noise. For any other form of random noise and for the systematic noise, you alter the total filtering behavior [...] Agreed. I wonder if not the filter properties of the burst average is altered compared to an evenly spread block, such that when treated as MDEV measures we have a difference. The burst filter-properties should be similar to that of PWM over the burst repetition rate. I just contradicted myself. I will come back to this topic, one has to be careful as filter properties will color the result and biases can occur. Most of these biases is usually not very useful, but the MDEV averaging is, if used correctly. Cheers, Magnus ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi Poul-Henning, On 08/01/2015 10:32 PM, Poul-Henning Kamp wrote: In message 49c4ccd3-09ce-48a4-82b8-9285a4381...@n1k.org, Bob Camp writes: The approach you are using is still a discrete time sampling approach. As such it does not directly violate the data requirements for ADEV or MADEV. As long as the sample burst is much shorter than the Tau you are after, this will be true. If the samples cover 1% of the Tau, it is very hard to demonstrate a noise spectrum that this process messes up. So this is where it gets interesting, because I suspect that your 1% lets play it safe threshold is overly pessimistic. I agree that there are other error processes than white PM which would get messed up by this and that general low-pass filtering would be much more suspect. But what bothers me is that as far as I can tell from real-life measurements, as long as the dominant noise process is white PM, even 99% Tau averaging gives me the right result. I have tried to find a way to plug this into the MVAR definition based on phase samples (Wikipedia's first formula under Definition) and as far as I can tell, it comes out the same in the end, provided I assume only white PM noise. I put that formula there, and I think Dave trimmed the text a little. For true white PM *random* noise you can move your phase samples around, but you gain nothing by bursting them. For any other form of random noise and for the systematic noise, you alter the total filtering behavior as compared to AVAR or MVAR, and it is through altering the frequency behavior rhat biases in values is born. MVAR itself has biases compared to AVAR for all noises due to its filtering behavior. The bursting that you propose is similar to the uneven spreading of samples you have in the dead-time sampling, where the time between the start-samples of your frequency measures is T, but the time between the start and stop samples of your frequency measures is tau. This creates a different coloring of the spectrum than if the stop sample of the previous frequency measure also is the start sample of the next frequency measure. This coloring then creates a bias-ing depending on the frequency spectrum of the noise (systematic or random), so you need to correct it with the appropriate biasing function. See the Allan deviation wikipedia article section of biasing functions and do read the original Dave Allan Feb 1966 article. For doing what you propose, you will have to define the time properities of the burst, so that woould need to have the time between the bursts (tau) and time between burst samples (alpha). You would also need to define the number of burst samples (O). You can define a bias function through analysis. However, you can sketch the behavior for various noises. For white random phase noise, there is no correlation between phase samples, which also makes the time between them un-interesting, so we can re-arrange our sampling for that noise as we seem fit. For other noises, you will create a coloring and I predict that the number of averaged samples O will be the filtering effect, but the time between samples should not be important. For systematic noise such as the quantization noise, you will again interact, and that with a filtering effect. At some times the filtering effect is useful, see MVAR and PVAR, but for many it becomes an uninteresting effect. But I have not found any references to this optimization anywhere and either I'm doing something wrong, or I'm doing something else wrong. I'd like to know which it is :-) You're doing it wrong. :) PS. At music festival, so quality references is at home. Cheers. Magnus ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
In message 55bdb002.8060...@rubidium.dyndns.org, Magnus Danielson writes: For true white PM *random* noise you can move your phase samples around, but you gain nothing by bursting them. I gain nothing mathematically, but in practical terms it would be a lot more manageable to record an average of 1000 measurements once per second, than 1000 measurements every second. For any other form of random noise and for the systematic noise, you alter the total filtering behavior [...] Agreed. -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
In message 55bc202f.6060...@gmail.com, Daniel Mendes writes: Can someone explain in very simple terms what this graph means? My current interpretation is as following: For a 100Hz input, if you look to your signal in 0.1s intervals, there´s about 1.0e-11 frequency error on average (RMS average?) Close: To a first approximation MVAR is the standard-deviation of the frequency, as a function of the time-interval you measure the frequency over. -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi If on the same graph you plotted the “low pass filter” response of your sample / average process, it would show how much / how little impact there likely is. It’s not any different than a standard circuit analysis. The old “poles at 10X frequency don’t count” rule. No measurement we ever make is 100% perfect, so a small impact does not immediately rule out an approach. Your measurement gets better by some number related to the number of samples. It might be square root of N, it could be something else. If it’s sqrt(N), a 100 sample burst is getting you an order of magnitude better number when you sample. You could go another 10X at 10K samples. A very real question comes up about “better” in this case. It probably does not improve accuracy, resolution, repeatability, and noise floor all to the same degree. At some point it improves some of those and makes your MADEV measurement less accurate. = Because we strive for perfection in our measurements, *anything* that impacts their accuracy is suspect. A very closely related (and classic) example is lowpass filtering in front of an ADEV measurement. People have questioned doing this back at least into the early 1970’s. There may have been earlier questions, if so I was not there to hear them. It took about 20 years to come up with a “blessed” filtering approach for ADEV. It still is suspect to some because it (obviously) changes the ADEV plot you get at the shortest tau. That kind of decades long debate makes getting a conclusive answer to a question like this unlikely. = The approach you are using is still a discrete time sampling approach. As such it does not directly violate the data requirements for ADEV or MADEV. As long as the sample burst is much shorter than the Tau you are after, this will be true. If the samples cover 1% of the Tau, it is very hard to demonstrate a noise spectrum that this process messes up. Put in the context of the circuit pole, you now are at 100X the design frequency. At that point it’s *way* less of a filter than the sort of vaguely documented ADEV pre-filtering that was going on for years and years ….. (names withheld to protect the guilty …) Is this in a back door way saying that these numbers probably are (at best) 1% of reading sorts of data? Yes indeed that’s an implicit part of my argument. If you have devices that repeat to three digits on multiple runs, this may not be the approach you would want to use. In 40 years of doing untold thousands these measurements I have yet to see devices (as opposed to instrument / measurement floors) that repeat to under 1% of reading. Bob On Jul 31, 2015, at 5:04 PM, Poul-Henning Kamp p...@phk.freebsd.dk wrote: If you look at the attached plot there are four datasets. And of course... Here it is: -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. allan.png___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi If you take more data (rate is faster) the noise floor of the data set at 1 second goes down as square root N. (speeds up 10X, noise down by ~1/3) Past a point, the resultant plot is not messed up by the process involved in the sampling. Bob On Jul 31, 2015, at 9:26 PM, Daniel Mendes dmend...@gmail.com wrote: Ok... time to show my lack of knowledge in public and ask a very simple question: Can someone explain in very simple terms what this graph means? My current interpretation is as following: For a 100Hz input, if you look to your signal in 0.1s intervals, there´s about 1.0e-11 frequency error on average (RMS average?) How far from the truth am I? Daniel Em 31/07/2015 18:04, Poul-Henning Kamp escreveu: If you look at the attached plot there are four datasets. And of course... Here it is: ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
In message 49c4ccd3-09ce-48a4-82b8-9285a4381...@n1k.org, Bob Camp writes: The approach you are using is still a discrete time sampling approach. As such it does not directly violate the data requirements for ADEV or MADEV. As long as the sample burst is much shorter than the Tau you are after, this will be true. If the samples cover 1% of the Tau, it is very hard to demonstrate a noise spectrum that this process messes up. So this is where it gets interesting, because I suspect that your 1% lets play it safe threshold is overly pessimistic. I agree that there are other error processes than white PM which would get messed up by this and that general low-pass filtering would be much more suspect. But what bothers me is that as far as I can tell from real-life measurements, as long as the dominant noise process is white PM, even 99% Tau averaging gives me the right result. I have tried to find a way to plug this into the MVAR definition based on phase samples (Wikipedia's first formula under Definition) and as far as I can tell, it comes out the same in the end, provided I assume only white PM noise. But I have not found any references to this optimization anywhere and either I'm doing something wrong, or I'm doing something else wrong. I'd like to know which it is :-) -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi On Aug 1, 2015, at 4:32 PM, Poul-Henning Kamp p...@phk.freebsd.dk wrote: In message 49c4ccd3-09ce-48a4-82b8-9285a4381...@n1k.org, Bob Camp writes: The approach you are using is still a discrete time sampling approach. As such it does not directly violate the data requirements for ADEV or MADEV. As long as the sample burst is much shorter than the Tau you are after, this will be true. If the samples cover 1% of the Tau, it is very hard to demonstrate a noise spectrum that this process messes up. So this is where it gets interesting, because I suspect that your 1% lets play it safe threshold is overly pessimistic. I completely agree with that. It’s more a limit that lets you do *some* sampling but steers clear of any real challenge to the method. I agree that there are other error processes than white PM which would get messed up by this and that general low-pass filtering would be much more suspect. But what bothers me is that as far as I can tell from real-life measurements, as long as the dominant noise process is white PM, even 99% Tau averaging gives me the right result. Indeed a number of people noticed this with low pass filtering …. back a number (~1975) of years ago…. The key point being that white PM is the dominant noise process. If you have a discrete spur in there, it will indeed make a difference. You can fairly easily construct a sample averaging process that drops a zero on a spur (average over a exactly a full period …). How that works with discontinuous sampling is not quite as clean as how it works with a continuous sample (you now average over N out of M periods… ). I have tried to find a way to plug this into the MVAR definition based on phase samples (Wikipedia's first formula under Definition) and as far as I can tell, it comes out the same in the end, provided I assume only white PM noise. Which is why *very* sharp people debated filtering on ADEV for years before anything really got even partial settled. But I have not found any references to this optimization anywhere and either I'm doing something wrong, or I'm doing something else wrong. I'd like to know which it is :-) Well umm …. errr …. *some* people have been known to simply document what they do. They then demonstrate that for normal noise processes it’s not an issue. Do an “adequate” number of real world comparisons and then move on with it. There are some pretty big names in the business that have gone that route. Some of them are often referred to with three and four letter initials …. In this case probably note the issue (or advantage !!) with discrete spurs and move on. If you are looking for real fun, I would dig out Stein’s paper on pre-filtering and ADEV. That would give you you a starting point and a framework to extend to MADEV. Truth in lending: The whole discrete spur thing described above is entirely from work on a very similar problem. I have *not* proven it with your sampling approach. Bob -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Ok... time to show my lack of knowledge in public and ask a very simple question: Can someone explain in very simple terms what this graph means? My current interpretation is as following: For a 100Hz input, if you look to your signal in 0.1s intervals, there´s about 1.0e-11 frequency error on average (RMS average?) How far from the truth am I? Daniel Em 31/07/2015 18:04, Poul-Henning Kamp escreveu: If you look at the attached plot there are four datasets. And of course... Here it is: ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
If you look at the attached plot there are four datasets. And of course... Here it is: -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Shouldn't the quantization/ measurement noise power be easy to measure? So I guess I havn't explained my idea well enough yet. If you look at the attached plot there are four datasets. 100Hz, 10Hz and 1Hz are the result of collecting TI measurements at these rates. As expected the X^(3/2) slope white PM noise is reduced by sqrt(10) every time we increase the measurement frequency by a factor 10. The 1Hz 10avg dataset is where the HP5370 does 10 measurements as fast as possible, once per second, and returns the average. The key observation here is I get the same sqrt(10) improvement without having to capture, store and process 10 times as many datapoints. Obviously I learn nothing about the Tau [0.1 ... 1.0] range, but as you can see, that's not really a loss in this case. *If* this method is valid, possibly conditioned on paying attention to the counters STDDEV calculation... and *If* we can get the turbo-5370 to give us an average of 5000 measurements once every second. *Then* the PM noise curtain drops from 5e-11 to 7e-13 @ Tau=1s Poul-Henning PS: The above plot is made by processing a single 100 Hz raw data file which is ny new HP5065 against an GPSDO. -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi James, On 07/30/2015 06:34 PM, James Peroulas wrote: My understanding is that MVAR(m*tau0) is equivalent to filtering the phase samples x(n) by averaging m samples to produce x'(n) [x'(n)=1/m*(x(n)+x(n+1)..x(n+m-1))] and then calculating AVAR for tau=m*tau0 on the filtered sequence. Thus, MVAR already performs an averaging/ lowpass filtering operation. Adding another averaging filter prior to calculating MVAR would seem to be defining a new type of stability measurement. Yes, fhat's how MVAR works. Not familiar with the 5370... Is it possible to configure it to average measurements over the complete tau0 interval with no dead time between measurements? Assuming the 5370 can average 100 evenly spaced measurements within the measurement interval (1s?), calculating MVAR on the captured sequence would produce MVAR(m*.01)) for m being a multiple of 100. i.e., tau0 here is actually .01, not 1, but values for MVAR(tau) for tau's less than 1s are not available. The stock 5370 isn't a great tool for this. The accelerator board that replaces the CPU and allows for us to add algorithms, makes the counter hardware much more adapted for this setup. Shouldn't the quantization/ measurement noise power be easy to measure? Can't it just be subtracted from the MVAR plot? I've done this with AVAR in the past to produce 'seemingly' meaningful results (i.e. I'm not an expert). You can curve-fit an estimation of that noise and remove it from the plot. For lower taus the confidence intervals will suffer in practice. I calculated the PSD of x(n) and it was clear where the measurements were being limited by noise (flat section at higher frequencies). From this I was able to estimate the measurement noise power. It is. Notice that some of it is noise and some is noise-like systematics from the quantization. AVAR_MEASURED(tau)=AVAR_CUT(tau)+AVAR_REF(tau)+AVAR_MEAS(tau) i.e. The measured AVAR is equal to the sum of the AVAR of the clock under test (CUT), the AVAR of the reference clock, and the AVAR of the measurement noise. If the reference clock is much better than the CUT AVAR_REF(tau) can be ignored. AVAR_MEAS(tau) is known from the PSD of x(n) and can be subtracted from AVAR_MEASURED(tau) to produce a better estimate of AVAR_CUT(tau). Depending on the confidence intervals of AVAR_MEASURED(tau) and the noise power estimate, you can get varying degrees of cancellation. 10dB of improvement seemed quite easy to obtain. Using the Lambda counter approach, filtering with the average blocks of Modified Allan Variance, makes the white phase noise slope go 1/tau^3 rather than 1/tau^2 as it is for normal Allan Variance. This means that the limiting slope of the white noise will cut over to the actual noise for lower tau. so that is an important tool already there. Also, it achieves it with known properties in confidence intervals. Using the Omega counter approach, you can get further improvements by about 1.25 dB, which is then deemed optimal as the Omega counter method is a linear regression / least square method for estimating the frequency samples and then those is used for AVAR processing. The next trick to pull is to do cross correlation of two independent channels, so that their noise does not correlate. This can help for some of it, but systematics can become a limiting factor. Cheers, Magnus James Message: 7 Date: Tue, 28 Jul 2015 21:51:07 + From: Poul-Henning Kamp p...@phk.freebsd.dk To: time-nuts@febo.com Subject: [time-nuts] Modified Allan Deviation and counter averaging Message-ID: 2884.1438120...@critter.freebsd.dk Content-Type: text/plain; charset=us-ascii Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later
Re: [time-nuts] Modified Allan Deviation and counter averaging
My understanding is that MVAR(m*tau0) is equivalent to filtering the phase samples x(n) by averaging m samples to produce x'(n) [x'(n)=1/m*(x(n)+x(n+1)..x(n+m-1))] and then calculating AVAR for tau=m*tau0 on the filtered sequence. Thus, MVAR already performs an averaging/ lowpass filtering operation. Adding another averaging filter prior to calculating MVAR would seem to be defining a new type of stability measurement. Not familiar with the 5370... Is it possible to configure it to average measurements over the complete tau0 interval with no dead time between measurements? Assuming the 5370 can average 100 evenly spaced measurements within the measurement interval (1s?), calculating MVAR on the captured sequence would produce MVAR(m*.01)) for m being a multiple of 100. i.e., tau0 here is actually .01, not 1, but values for MVAR(tau) for tau's less than 1s are not available. Shouldn't the quantization/ measurement noise power be easy to measure? Can't it just be subtracted from the MVAR plot? I've done this with AVAR in the past to produce 'seemingly' meaningful results (i.e. I'm not an expert). I calculated the PSD of x(n) and it was clear where the measurements were being limited by noise (flat section at higher frequencies). From this I was able to estimate the measurement noise power. AVAR_MEASURED(tau)=AVAR_CUT(tau)+AVAR_REF(tau)+AVAR_MEAS(tau) i.e. The measured AVAR is equal to the sum of the AVAR of the clock under test (CUT), the AVAR of the reference clock, and the AVAR of the measurement noise. If the reference clock is much better than the CUT AVAR_REF(tau) can be ignored. AVAR_MEAS(tau) is known from the PSD of x(n) and can be subtracted from AVAR_MEASURED(tau) to produce a better estimate of AVAR_CUT(tau). Depending on the confidence intervals of AVAR_MEASURED(tau) and the noise power estimate, you can get varying degrees of cancellation. 10dB of improvement seemed quite easy to obtain. James Message: 7 Date: Tue, 28 Jul 2015 21:51:07 + From: Poul-Henning Kamp p...@phk.freebsd.dk To: time-nuts@febo.com Subject: [time-nuts] Modified Allan Deviation and counter averaging Message-ID: 2884.1438120...@critter.freebsd.dk Content-Type: text/plain; charset=us-ascii Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything
Re: [time-nuts] Modified Allan Deviation and counter averaging
*Very* much looking forward to some insight on this method - I've done pretty much the same experiment with an E1740A (48.8ps resolution); trigger from a z3805A PPS, start TI measurement on the first rising edge of the z3805A 10Mhz (on channel 1) following the trigger, stop the TI measurement on (i.e.) the 30th rising edge on whatever signal is on channel 2. Capture 100 samples gap-free every trigger, and average as described. It is severely bandwidth-limited by the GPIB interface as to the number of samples that can be collected on a per second basis, but the E1740A can store up to ~500k samples, so it can all be batch processed after the measurement is complete. It will be very interesting to see if the method has any merit. Ole On Tue, Jul 28, 2015 at 11:51 PM, Poul-Henning Kamp p...@phk.freebsd.dk wrote: Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
If the burst rate is high enough the interpolators will no longer be able to maintain phase lock, the question is how high a burst rate is feasible without losing lock? Bruce On Wednesday, 29 July 2015 9:51 AM, Poul-Henning Kamp p...@phk.freebsd..dk wrote: Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
Re: [time-nuts] Modified Allan Deviation and counter averaging
Good morning, On 07/28/2015 11:51 PM, Poul-Henning Kamp wrote: Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. One of the papers that I referenced in my long post on PDEV has a nice section on that boundary, worth reading. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. The Turbo-5370 creates an interesting platform for us to play with, indeed. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. You rang. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? You *will* get an improvement in your response, yes, but it will not pan out as you would like it to. What you create is, just as with the Lambda and Omega counters, a pre-filter mechanism that comes before the ADEV or MDEV filtering prior to squaring. The filtering aims at filtering out white noise or (systematic) noise behaving like white noise. The filtering method was proposed by J.J. Snyder in 1980 for laser processing and further in his 1981 paper, published at the same time as Allan et al published the MDEV/MVAR paper that is directly inspired by Snyders work. Snyder shows that rather than getting a MVAR slope of 1/tau^2 you get a slope of 1/tau^3 for the ADEV estimation. This slope change comes from the changing of the system bandwidth, as the tau increases rather than the classical method of having a fixed system bandwidth and then process ADEV on that. MDEV/MVAR uses a software filter to alter the system bandwidth along with tau, and thus provide a fix for the 15 year hole of not being able to separate white and flicker phase noise that Dave Allan was so annoyed with. Anyway, the key point here is that the filter bandwidth changes with tau. The form of discussions we have had on Lambda (53132) and Omega (CNT-90) counters and the hockey-puck response they create is because they have a fixed pre-filtering bandwidth. What you proposes is a similar form of weighing mechanism providing a similar type of filtering mechanism and a similar fixed pre-filtering bandwidth and thus causing a similary type of response. Just as with the Lambda and Gamma counters, using MDEV rather than ADEV does not really heal the problem, since for longer taus, you observe signals in the pass-band of the fixed low-pass-filter and your behavior have gone back to the behavior of the ADEV or MDEV of the counter without the filtering mechanism. This is the unescapable fact. To solve this, you will have to build a pre-filtering mechanism that allows itself to be
Re: [time-nuts] Modified Allan Deviation and counter averaging
Hi The “simple” way to see what this does is to plug your sampling approach into the DSP world and see what sort of filter it creates. Put another way - would you use this to make a lowpass filter? If so how good a filter would it be? At least with a hand wave, I would not deliberately use this process as a lowpass. Since it averages, it does have lowpass properties. Until you get to the point that you are averaging a cycle, it’s not got *much* of a lowpass. It’s a discontinuous boxcar, you likely will get a few zeros in the passband around the width of your sample window. Now the question: Does this lowpass (crummy though it is) impact your noise? Well sure, to some degree it does. The big way it would is if those zeros (which probably aren’t as deep as you might think) line up with a messy spike in your noise. So where are the zeros? If you take 100 samples of a 100 ns period waveform, your boxcar is 10 us wide. Expect zeros (or at least dips) at 100KHz XN. Move the samples up to 1,000 and your dips are at 10 KHz X N. I’d suggest that most “normal noise” spectra are going to look pretty much the same with and without what’s been taken away. Now, take it up to 10% of the total 1 second window and you are at 10Hz. Those zeros *are* going to mess with your spectra and likely will impact your 1 second MVAR or AVAR or whatever. All that *assumes* you are taking a reading every cycle with the uber-5370. If you are doing it old school, your sampling process will wrap the noise spectrum a bit. That’s going to make the analysis a bit more messy. Bob On Jul 28, 2015, at 5:51 PM, Poul-Henning Kamp p...@phk.freebsd.dk wrote: Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there. ___ time-nuts mailing list --
Re: [time-nuts] Modified Allan Deviation and counter averaging
Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? I think this is basically a valid thing to do, under specific conditions. It works well with the Wavecrest DTS boxes, which can take several thousand samples per second and distill them down to a single TI reading. I've observed good agreement between ADEV plots taken with the DTS2070 and direct-digital analyzers down to the ~2E-12 level at t=1s. The main caveat is that the burst of averaged readings needs to take up a very small portion of the tau-zero interval, as you point out, to keep the averaging effects out of the visible portion of the plot. This might not be a very big problem with MDEV but it is definitely an issue with ADEV. The second thing to watch for is even more important: while the host software (TimeLab, Stable32, etc.) can handle phase wraps that occur between counter readings, the sub-reading averaging algorithm in the counter will not. Phase wraps in the middle of a burst will corrupt the data badly, and I don't see any reliable way to detect and unfold them after the fact. So if John's firmware can handle intra-burst phase wraps before returning each averaged reading to the software, it could be a big win. Otherwise this technique should be confined to cases where two extremely stable sources are being compared with no possibility of phase wraps. It would be a good way to keep an eye on the short-term behavior of a pair of Cs or GPS clocks, for instance. -- john, KE5FX Miles Design LLC ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
[time-nuts] Modified Allan Deviation and counter averaging
Sorry this is a bit long-ish, but I figure I'm saving time putting in all the details up front. The canonical time-nut way to set up a MVAR measurement is to feed two sources to a HP5370 and measure the time interval between their zero crossings often enough to resolve any phase ambiguities caused by frequency differences. The computer unfolds the phase wrap-arounds, and calculates the MVAR using the measurement rate, typically 100, 10 or 1 Hz, as the minimum Tau. However, the HP5370 has noise-floor in the low picoseconds, which creates the well known diagonal left bound on what we can measure this way. So it is tempting to do this instead: Every measurement period, we let the HP5370 do a burst of 100 measurements[*] and feed the average to MVAR, and push the diagonal line an order of magnitude (sqrt(100)) further down. At its specified rate, the HP5370 will take 1/30th of a second to do a 100 sample average measurement. If we are measuring once each second, that's only 3% of the Tau. No measurement is ever instantaneous, simply because the two zero crossings are not happening right at the mesurement epoch. If I measure two 10MHz signals the canonical way, the first zero crossing could come as late as 100(+epsilon) nanoseconds after the epoch, and the second as much as 100(+epsilon) nanoseconds later. An actual point of the measurement doesn't even exist, but picking with the midpoint we get an average delay of 75ns, worst case 150ns. That works out to one part in 13 million which is a lot less than 3%, but certainly not zero as the MVAR formula pressume. Eyeballing it, 3% is well below the reproducibility I see on MVAR measurements, and I have therefore waved the method and result through, without a formal proof. However, I have very carefully made sure to never show anybody any of these plots because of the lack of proof. Thanks to Johns Turbo-5370 we can do burst measurements at much higher rates than 3000/s, and thus potentially push the diagonal limit more than a decade to the left, while still doing minimum violence to the mathematical assumptions under MVAR. [*] The footnote is this: The HP5370 firwmare does not make triggered bust averages an easy measurement, but we can change that, in particular with Johns Turbo-5370. But before I attempt to do that, I would appreciate if a couple of the more math-savy time-nuts could ponder the soundness of the concept. Apart from the delayed measurement point, I have not been able to identify any issues. The frequency spectrum filtered out by the averaging is wy to the left of our minimum Tau. Phase wrap-around inside bursts can be detected and unfolded in the processing. Am I overlooking anything ? -- Poul-Henning Kamp | UNIX since Zilog Zeus 3.20 p...@freebsd.org | TCP/IP since RFC 956 FreeBSD committer | BSD since 4.3-tahoe Never attribute to malice what can adequately be explained by incompetence. ___ time-nuts mailing list -- time-nuts@febo.com To unsubscribe, go to https://www.febo.com/cgi-bin/mailman/listinfo/time-nuts and follow the instructions there.
