Sampling a signal and do the discrete Fourier transform

In summary, increasing the sampling frequency results in a finer fast Fourier transform of the sampled signal. However, the Fourier transform of the sampled signal is much narrower compared to the original signal. This phenomenon can be explained by the fact that doubling the sampling frequency also doubles the frequency range of the DTFT, which is not the case for the CTFT. The DTFTs are plotted against normalised (angular) frequency, causing confusion, but this can be resolved by plotting against (angular) frequency instead.
  • #1
wybmax
7
0
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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  • #2
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.
 
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  • #3
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.
 

1. What is sampling a signal?

Sampling a signal refers to the process of taking measurements or samples of a continuous signal at regular intervals. This is necessary in order to convert an analog signal into a digital signal that can be analyzed and processed using computers.

2. Why is it necessary to sample a signal?

Sampling a signal is necessary because computers can only process digital signals, which are made up of discrete values. By sampling a continuous signal, we can convert it into a series of discrete values that can be stored, manipulated, and analyzed using digital systems.

3. What is the discrete Fourier transform (DFT)?

The discrete Fourier transform is a mathematical algorithm used to convert a discrete time-domain signal into its corresponding frequency-domain representation. This allows us to analyze the frequency content of a signal and identify any underlying patterns or components.

4. How does the DFT work?

The DFT works by taking a series of discrete samples of a signal and converting them into complex numbers. These numbers represent the magnitude and phase of each frequency component in the signal. By combining these numbers, we can reconstruct the original signal in the frequency domain.

5. What are some applications of sampling and the DFT?

Sampling and the DFT have numerous applications in various fields such as signal processing, communications, audio and image processing, and data compression. They are also used in scientific research to analyze and understand different types of signals, such as biological signals, weather data, and financial data.

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