>On 24 Mrz., 11:22, "fittipaldi" <em.fittipa...@abv.bg> wrote:
>> Let's assume I have an idea for a 1024 point filter. I know how the
>> frequency response should be: all points between 100 and 924 are 0 an
all
>> other 1 (I think this should be a perfect low-pass (or maybe not?). S
I
>> know the f.response. Now how to filter my signal? What I think is
>> following:
>>
>> 1.) IFFT convert the frequency response to impulse response (from
>> frequency to time domain);
>> 2.) apply some kind of window (hamming for example) by multiplying al
the
>> points after the IFFT with the formula of the window;
>> 3.) now convolute with the signal I receive in time domain (b
multiplying
>> and summing all the signal's points from 1024 upwards with every poin
from
>> the filter).
>> 4.) What I received is the filtered signal ...
>>
>> Is this true?
>
>You can use the FFT to design filters and also to apply them
>efficiently. For designing I would use a larger "FFT size" (for
>example 8192) and reasonably smooth magnitude response (no quick
>jumps) to fight wrap-around errors, apply the iFFT, apply window
>function (now of length 1024). Et voila!
>
>But in your case (lowpass filter) you should use the "windowed sinc"
>design method. It's "equivalent" to the procedure above for "FFT-size
>= infinity". ( http://www.dspguide.com/ch16.htm )
>
>You may also be interested in the properties of various window
>functions
>( http://en.wikipedia.org/wiki/Window_function )
>
>Cheers!
>SG
>
>

Yep, I use exactly the method from this book. My problem is I know th
frequency response and want to substitute in K (if we read chapter 16), th
sinc function, with my own filter idea ...