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 commentaires

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

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Greg Dionne
Greg Dionne le 29 Juin 2016
Modifié(e) : Greg Dionne le 29 Juin 2016

0 votes

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 commentaire

kauerbach
kauerbach le 29 Juin 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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