Sampling a signal and do the discrete Fourier transform

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SUMMARY

The discussion centers on the relationship between sampling frequency and the discrete Fourier transform (DFT) of a digital signal. As the sampling frequency increases, the fast Fourier transform (FFT) of the sampled signal becomes finer, leading to a narrower frequency representation. The Nyquist theorem indicates that doubling the sampling frequency also doubles the frequency range of the discrete-time Fourier transform (DTFT). Additionally, the X-axis labeling of the DTFT in radians is misleading, as frequency should be represented in rad/sec to avoid confusion.

PREREQUISITES
  • Understanding of the Nyquist theorem
  • Familiarity with fast Fourier transform (FFT) techniques
  • Knowledge of discrete-time Fourier transform (DTFT) concepts
  • Basic principles of signal sampling
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  • Study the Nyquist-Shannon sampling theorem in detail
  • Learn how to implement fast Fourier transform (FFT) in Python using NumPy
  • Explore the differences between continuous-time Fourier transform (CTFT) and discrete-time Fourier transform (DTFT)
  • Investigate common pitfalls in signal processing related to frequency representation
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Signal processing engineers, digital signal processing (DSP) students, and anyone involved in analyzing or reconstructing sampled signals will benefit from this discussion.

wybmax
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When I sample a certain digital signal with increasing sampling frequency, the fast Fourier transform of the sampled signal becomes finer and finer. (the image follows) Previously I thought higher sampling frequency makes the sampled signal more similar to the original one, so the Fourier transform of a signal sampled at very high frequency would be the same as the FT of the original signal. But in fact the FT of the sampled signal is much narrower.

How to explain this phenomenon? As the FT is different in different sampling conditions, why the original signal can still be correctly reconstructed?
 

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If you doubled the sampling frequency, the frequency range of the DTFT doubles (because the Nyquist frequency is double).

The X axis of your CTFT plot is in rad/sec. The X axis of your DTFT plot is labelled in rad, which is wrong, because "radians" are not a unit for frequency. If you convert the DTFT scales into rad/sec, the plots will look the same - except that the second one covers twice the frequency range of the first one.

You can extend the CTFT plot to cover any frequency range you like, of course.
 
Last edited:
AlephZero said:
The X axis of your DTFT plot is labelled in rad, which is wrong, because "radians" are not a unit for frequency.
I wouldn't say this is "wrong", but it is what's causing the confusion. The DTFTs are plotted against normalised (angular) frequency instead of (angular) frequency. When plotted against (angular) frequency, the confusion should be removed.
 

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