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.

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Star Strider
Star Strider 2016 年 6 月 29 日
How did you calculate ‘powerSpectrum’ and ‘freq’?
kauerbach
kauerbach 2016 年 6 月 29 日
I applied easyspec.m to the measured waveform:

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Greg Dionne
Greg Dionne 2016 年 6 月 29 日
編集済み: Greg Dionne 2016 年 6 月 29 日

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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?

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kauerbach
kauerbach 2016 年 6 月 29 日
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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