Cleaning Up Signal Distortion Using FFT: Devising an Effective Method

In summary, the problem at hand is to clean up a signal h(t) that is a product of two functions, v(t) and g(t), where g(t) is a low frequency noise. One possible solution is to use the FFT and inverse FFT to remove the lower frequencies, but this might also remove v(t). This is an inverse problem and requires some knowledge of the lowest frequencies present in v(t) to choose an appropriate filter. However, since h(t) is not an actual experiment, this approach may not be feasible. Another possibility is to use Wiener filtering method to extract v(t).
  • #1
singedang2
26
0

Homework Statement


hello!

i'm given a signal h(t) = v(t)*g(t)
where g(t) is a distortion/noise that got added
and has a very low frequency compared to v(t)

i need to devise a method to clean up g(t)


The Attempt at a Solution



i'm thinking of to do the fft on the signal h(t),
and remove the lower frequencies and do the inverse fft,
but this might not just remove g(t), but it may as well remove v(t)

any hints?
 
Physics news on Phys.org
  • #2
This is called inverse problem and its an ill-posed problem. There are many methods especially Wiener filtering method to extract v(t).
 
  • #3
singedang2 said:

The Attempt at a Solution



i'm thinking of to do the fft on the signal h(t),
and remove the lower frequencies and do the inverse fft,
but this might not just remove g(t), but it may as well remove v(t)

any hints?
You would have to have some idea of the lowest frequencies present in v(t). Only then can you choose a suitable high-pass filter.

Have you taken a look at the FFT of h(t) yet?
 
  • #4
Redbelly98 said:
You would have to have some idea of the lowest frequencies present in v(t). Only then can you choose a suitable high-pass filter.

Have you taken a look at the FFT of h(t) yet?

i'm not actually given a function h(t).
which I mean we're not doing an actual experiment, where I get h(t) and to try to recover v(t).

i'm learning fft and Fourier analysis in school, and this is just one of the questions that is application of fft.

problem is, my idea of doing fft to h(t) and remove lower frequencies and do ifft might just remove both v(t) and d(t)

I need to somehow process h(t), so that, when I fft it, it separates well, and able to remove lower frequencies that are v(t) only.

btw * is a multiplication from the h(t) I've written. it looks kinda similar to convolution operator and makes it confusing
 
Last edited:
  • #5
In practice, you would look at the spectrum and make a judgement about where the low frequency limit of v(t) is. Then you could choose a suitable cutoff frequency for the filter.
 

What is signal processing fft?

Signal processing fft, or Fast Fourier Transform, is a mathematical algorithm used to convert a signal from its original time domain representation to a frequency domain representation. This allows for the analysis of the signal's frequency components and is commonly used in fields such as audio and image processing.

How does the fft algorithm work?

The fft algorithm works by breaking down a signal into its individual frequency components. It does this by taking the original signal and creating a series of smaller signals, each representing a different frequency range. These smaller signals are then combined to create the full frequency domain representation of the original signal.

What is the difference between fft and dft?

FFT, or Fast Fourier Transform, is an optimized version of DFT, or Discrete Fourier Transform. While both algorithms perform the same task of converting a signal from time domain to frequency domain, FFT is much faster due to its use of symmetry properties and efficient data handling techniques.

What are the applications of signal processing fft?

Signal processing fft has a wide range of applications, including audio and image processing, data compression, communication systems, and medical imaging. It is also commonly used in scientific research, such as in the analysis of earthquake signals or astronomical data.

What are some common misconceptions about signal processing fft?

One common misconception about signal processing fft is that it can create new information or enhance the quality of the original signal. In reality, fft simply breaks down the existing signal into its frequency components and does not add any new information. Additionally, some may believe that fft is only applicable to audio signals, when in fact it can be used for any type of signal that can be represented in the time domain.

Similar threads

Replies
6
Views
967
  • Electrical Engineering
Replies
4
Views
290
  • Electrical Engineering
Replies
9
Views
3K
  • Calculus and Beyond Homework Help
Replies
3
Views
914
  • Engineering and Comp Sci Homework Help
Replies
3
Views
1K
  • Engineering and Comp Sci Homework Help
Replies
2
Views
2K
  • MATLAB, Maple, Mathematica, LaTeX
Replies
4
Views
3K
  • MATLAB, Maple, Mathematica, LaTeX
Replies
1
Views
737
Replies
1
Views
1K
Replies
131
Views
4K
Back
Top