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FIRfilter coefficients can be calculated by taking the inverse Fourier transform of the desired frequency response and throwing away the small values:
- pretend we don't mind lots of filter coefficients
- specify the desired frequency response using lots of samples
- calculate the inverse Fourier transform
- this gives us a lot of filter coefficients
- so truncate the filter coefficients to give us less
- then calculate the Fourier transform of the truncated set of coefficients to see if it still matches our requirement
BUT...
Truncating the filter coefficients means we have a truncated signal. And a truncated signal has a broad frequency spectrum:
So truncating the filter coefficients means the filter's frequency response can only be defined coarsely.
Luckily, we already know a way to sharpen up the frequency spectrum of a truncated signal, by applying a window function. So after truncation, we can apply a window function to sharpen up the filter's frequency response:
So here is an even better recipe for calculating FIR filter coefficients:
- pretend we don't mind lots of filter coefficients
- specify the desired frequency response using lots of samples
- calculate the inverse Fourier transform
- this gives us a lot of filter coefficients
- so truncate the filter coefficients to give us less
- apply a window function to sharpen up the filter's frequency response
- then calculate the Fourier transform of the truncated set of coefficients to see if it still matches our requirement
This is called the window method of FIR filter design.
BUT...
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