How should I calculate power spectral density of signal with too high sampling rate
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Dear All,
I have a signal with high sampling rate (10000 Hz) and I would like to calculate power spectral density in the frequency range 0-50 Hz. Therefore, sampling rate 100 Hz would be sufficient.
My question is whether signal should be resampled by averaging 100 samples and then the spectrum should be calculated, or the spectrum should be calculated on the original signal. Should the results be the same? If not, which one would be more correct?
Best, Urban
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Accepted Answer
Honglei Chen
on 10 Oct 2014
Edited: Honglei Chen
on 10 Oct 2014
I would recommend first design a filter, to filter at 100Hz, then down sample/resample, and then do PSD calculation. Actually I believe resample does that anti-aliasing for you already.
In my opinion, doing computation on the original signal is unnecessary and expensive. You may also be able to get better resolution within 0-50Hz via resample since again it's more computational friendly to zero pad a low sampling rate signal.
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Image Analyst
on 10 Oct 2014
Yeah but it would reduce your resolution. I agree with Star, just do the whole thing and crop out or look at just the range you want. Especially since you have such a tiny amount of data (I'm assuming your data doesn't amount to hundreds of millions, or billions, of samples or anything). You might use pwelch() in the Signal Processing Toolbox, which is what the Mathworks recommended in the latest spectral filtering webinar, rather than fft.
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Urban
on 10 Oct 2014
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Image Analyst
on 10 Oct 2014
Well if you have a periodic signal that gets wiped out when you blur/average with a window size of 100 then the spike will go away. Of course you know that your frequency axis is now changed so you can't look at the same number of elements from pwelch and assume they correspond to the same frequency range as the original full signal.
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