Hi again,

Scott Dattalo wrote:
>I posted an analysis of the frequency response of this filter back in
Jan '00. I
>don't have time to search the archives now. If you're going to perform
any
>matlab analysis this could be of use.

Cant seem to find it ? ( not in january atleast ).

Anyway my initial findings indicate that these filters are
, as stated by others, not equal. They have different freq. 
responce and calculation properties. 
The FIR I implemented is, albeit memory hungry, very fast
to calculate and have a linear step-responce.
( one additon and one subtraction/sample after initialisation )
I will look at the 'twist' routine by Scott and see what could
come out of it with some modifications to 24 bit, but I doubt
it will be more suitable than the original FIR. 
I'm concered about the non-linear step-reponce in the IIR filter,
but it might not matter in the end.

But I'm by no means literate in filtering so I'm probably wrong :-).


Robert wrote:

>2. If you are concerned about the increased error in the first few
samples, 
>you can generate a slightly more complicated optimum (Kalman) filter.
(This 
>involves a changing weighting factor).

Do you mean that the weighing should leans towards the new sample
more at the start ? In that case that was my idea, I would also
use this in the 'running' filter to increase the step-repsonse.
( would have a similar effect as the variable window in the FIR )


>
>3. The optimum value for alpha is about equal in magnitude to the
1-order 
>autocorrelation of the data.

Sorry, dont know if it's my english, but I dont really understand what
You mean here.


Nice to read all the comments though, never to late to learn.


/Tony





Tony KŸbek, Flintab AB            
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E-mail: tony.kubek@flintab.com
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