This is an interesting idea. We often said in the early days of LF that LF
"noise" was not Gaussian.
I do remember attending a lecture at work by one of the resident maths team.
(Pitching for customers) He was considering telecom "noise" not radio. But
he demonstrated the standard processes, which assume Gaussian noise were not
effective on his example. He then introduced a "new" parameter he called
"burstiness". He was using Burst Error Rate as a metric, I think, rather
than S/N but the ideas were quite interesting. He was also investigating
different error-detection/correction algorithms against the "burstness"
index.. I am afraid it was mid 1980s and I dont remember much detail (or
even a name to follow for references) A few faces in the audience were quite
blank, one or two were leaning forward and some sprawled backward as if to
say "What's the problem, if its noisy, close the door" :-))
Alan
G3NYK
----- Original Message -----
From: "DK7FC" <[email protected]>
To: <[email protected]>
Sent: Thursday, November 03, 2016 6:37 PM
Subject: Re: LF: Smart noise cancelling?!?
Hi Peter,
That sounds interesting and looks convincing. But it only works for
'compressed' spectra covering several kHz and hours, right?
As far as i understand this method can't be done for weak signal
detections because you 'need' all the energy available from the weak
signal to fill the bin (one pixel) and just throw away what causes a
reduction of the SNR, so just the stronger QRN bursts are blanked and most
of the signal is coming through whereas your method selects just a small
fraction of the incoming energy (?).
My idea/question came from the consideration that different kind of QRN
has different optimum blanker settings.
Am 02.11.2016 21:25, schrieb Paul Nicholson:
Even more aggressive sferic blanking raises
the decode to Eb/N0 +1.7 dB BER 38.2%
S/N 16.10 dB in 25.4 uHz.
So, if propagation changes, the optimal blanker settings will change. So
they vary all the time. If a post-processing of a transmission/recording
taking several hours and night/day changes, it could be useful to
dynamically vary the blanker settings. So these blanker setting levels
would have to be determined/calculated by the incoming signal and then
applied in a next step.
This may be CPU-load intensive, i don't know, it's behind my current
skills. But the idea is there...
73, Stefan
Am 03.11.2016 18:55, schrieb Peter:
Hi Stefan et al.,
On 03.11.2016 14:22, DK7FC wrote:
...
Last night i thought a bit about noise cancelling on LF/VLF.
> ...
Running an experimental receiver at VLF/LF for SID-detection (on RPi 3) I
chose an almost non-parametric procedure running in frequency domain. It
works as follows:
Do a windowed FFT, compute the median (by sorting) from the power
spectrum. Store all spectra and corresponding median values. Next choose
a time period (let's say 100ms), pick the spectrum with the lowest
median, plot it, drop all the others. The key is that "Median values" are
more robust to outliers compared to other averaging procedures.
See what happened when switching from simple averaging to median
selection algorithm (~16:50 utc):
http://lf-radio.de/cgi-bin/test/show_wf.cgi?date=16-10-02
I know that this won't work in case of searching for coherent signal
detection, or would be hard to implement. But using this method I'm
detecting such very weak signals from far east like NDI or RTZ on a
regular basis.
Drawbacks? Yes; it's throwing away a lot of information which may be
useful. Another pitfall has to be mentioned: using 1 sec. as a selection
window strong time service transmitters nearly vanished since the
algorithm will unerringly choose the gaps [^_^]. Therefore I'm using only
the spectral part between 15 and 50 kHz for computing the median values.
Peter, df3lp
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