Greetings,
I am trying to estimate the occupied bandwidth of the measured signal. After post-processing, I arrived to the spectrum estimate "powerSpectrum.mat" and frequencies "freq.mat" (both files are attached).
load powerSpectrum.mat powerSpectrum
load freq.mat freq
figure
obw(powerSpectrum,freq);
The returned occupied bandwidth does not quite make sense to me. Is there anything that I did not do? or did wrong?
I would appreciate your help.

2 Kommentare

Star Strider
Star Strider am 29 Jun. 2016
How did you calculate ‘powerSpectrum’ and ‘freq’?
kauerbach
kauerbach am 29 Jun. 2016
I applied easyspec.m to the measured waveform:

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Greg Dionne
Greg Dionne am 29 Jun. 2016
Bearbeitet: Greg Dionne am 29 Jun. 2016

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You have a fair amount of noise power, which is swamping your measurement. To see what I mean try (assuming your spectrum is a PSD -- you were using the PSD syntax above):
plot(freq/1e9, cumsum(powerSpectrum)./mean(diff(freq)))
xlabel('Freq (GHz)')
ylabel('Cumulative Power (Watts)')
Note the large slope in cumulative power in the noise regions.
If you want to ignore the noise, try restricting the frequency band of interest. Something like:
obw(powerSpectrum, freq, [4.75 9]*1e9, 99)
Something looks awry in the band between 8.5 GHz - 9 GHz. Is this a real world signal?

1 Kommentar

kauerbach
kauerbach am 29 Jun. 2016
Thank you for your reply.
This was a real signal before denoising. The notch between 8.5 and 9 GHz probably occurred because one of the cut-off frequencies fell in the middle of the ripple. You can see reminiscences of other ripples on the peak itself.

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