[time-nuts] Long Wave Radio-Frequency standard testing

[Bob kb8tq]

Hi

Assuming the goal is a normal ADEV or xDEV sort of calculation:

If you replace the raw phase values with zero that can mess things up

0 seconds +20 ns
1 seconds +22 ns
2 seconds +23 ns
3 seconds +25 ns
4 seconds +27ns
5 seconds +29 ns

If you “loose” one of those 20 to 30 ns values and replace it with zero, you have significantly
changed the data set.

Even if you are looking at deltas, zero stuffing would be problematic with that
(contrived) phase data set. 

1 seconds +2
2 seconds +1
3 seconds +2
4 seconds +2
5 seconds +2

If the objective is something like a PLL then “hold at the last value” is the only practical
answer to the question. You don’t *have* the next value and you need to stuff something
into the control loop computation.

Bob


> On Jan 19, 2021, at 1:37 PM, Dave Daniel <kc0wjn at gmail.com> wrote:
> 
> Or one can replace those values with zero. That eliminates them; averaging then proceeds without those values altering the most probable correct average.
> 
> DaveD
> 
>> On Jan 19, 2021, at 08:49, Bob kb8tq <kb8tq at n1k.org> wrote:
>> 
>> Hi
>> 
>> The normal approach to filling a gap is to put in a point that is the average
>> of the two adjacent points. The assumption is that this is a “safe” value that
>> will not blow up the result. That’s probably ok if it is done rarely. The risk is
>> that you are running a filter process (averaging is a low pass filter). 
>> 
>> If you pull out a *lot* of outliers and replace them, you are doing a lot of filtering.
>> Since you are measuring noise, filtering is very likely to improve the result. 
>> The question becomes - how representative is the result after a lot of this or 
>> that has been done? 
>> 
>> Obviously the answer to all this depends on what you are trying to do. If you
>> are running a control loop and the output improves, that’s fine. If you are 
>> trying to provide an accurate measure of noise …. maybe not so much :) 
>> 
>> Bob
>> 
>>> On Jan 19, 2021, at 2:15 AM, Gilles Clement <clemgill at gmail.com> wrote:
>>> 
>>> Hi, 
>>> Yes outliers removal creates gap in Stable32.
>>> The « fill »  function can fills gaps with interpolated values. 
>>> It does not change much the graphs, except in the low Tau area (see attached). 
>>> Do you know a discussion of impact of outliers removal ? 
>>> Gilles. 
>>> 
>>> 
>>> 
>>>> Le 18 janv. 2021 à 22:06, Bob kb8tq <kb8tq at n1k.org> a écrit :
>>>> 
>>>> Hi
>>>> 
>>>> As you throw away samples that are far off the mean, you reduce the sample
>>>> rate ( or at least create gaps in the record). Dealing with that could be difficult.
>>>> 
>>>> Bob
>>>> 
>>>>>> On Jan 18, 2021, at 1:33 PM, Gilles Clement <clemgill at gmail.com> wrote:
>>>>>> 
>>>>>> Hi
>>>>>> 
>>>>>> Very cool !!!
>>>>>> 
>>>>>> The red trace is obviously the one to focus on. Some sort of digital loop that
>>>>>> only operates under the “known good” conditions would seem to make sense. 
>>>>>> 
>>>>>> Thanks for sharing 
>>>>>> 
>>>>>> Bob
>>>>> 
>>>>> Hi,
>>>>> I tried something with the idea to consider night records fluctuations as « outliers » as compared to day records. 
>>>>> Indeed the 3 days record mean value is flat and the histogram quite gaussian. 
>>>>> So I processed the 3 days record (green trace) with Stable32’s « Check Function »,
>>>>> while removing outliers with decreasing values of the Sigma Factor. The graph below shows the outcome. 
>>>>> The graph with Sigma=0.8 (blue trace) connects rather well with the 1Day record (red trace). 
>>>>> Would this be a workable approach ? 
>>>>> Best, 
>>>>> Gilles. 
>>>>> 
>>>>> 
>>>>> 
>>>>> 
>>>>> 
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