[PD] is there a kalman filter for pd? (if not I could probably provide one, if I make it :) )

[katja]

Thanks for the suggestion, Cyrille. I've been playing around with
median filters in a different context (spectral processing), but
completely forgot about them.

With the variometer, the problem is to isolate very low frequencies
(the pressure gradient you want to detect) from DC (constant
atmospheric pressure at certain height) and sensor noise frequencies.
And you want to see results with accuracy and little delay. In fact it
needs a very sharp minimum-phase filter. Maybe a median filter can
'preprocess' the signal in some way. Anyway it gives a new
perspective.

Katja



On Fri, Dec 14, 2012 at 1:27 PM, Cyrille Henry <ch at chnry.net> wrote:
> for sensors data, depending of the noise, it can be useful to begin with a
> median filter.
> a median on the 7 last sample add 3 sample delay, but often remove lot's of
> noise.
>
> you can find them in mapping or puremapping libs.
> cheers
> cyrille
>
>
> Le 14/12/2012 11:38, katja a écrit :
>
>> Patrick, the barometer sensor samplerate is ~50 Hz and I did the
>> butterworth filter with regular Pd objects, not as external (see
>> attached).
>>
>> In the Pd patch I modeled sensor noise (resolution 3 Pascal according
>> to datasheet) and pressure gradient, simulating vertical speed through
>> the air. The aim is to get 0.1 m/s accuracy in vertical speed reading.
>> Theoretically, this would be almost possible with the butterworth. But
>> our real sensor has much more noise than 3 Pascal resolution.
>> Therefore I'm still interested in better filters.
>>
>> Katja
>>
>>
>> On Wed, Dec 12, 2012 at 6:29 PM, patrick <puredata at 11h11.com> wrote:
>>>
>>> hi Katja,
>>>
>>> did you ported this filter:
>>> https://github.com/lebipbip/le-BipBip/blob/master/filter.c
>>>
>>> to an pd external? if yes could you share it? not sure if it would help
>>> my
>>> situation (noisy accelerometer 1 axis), but i would like to give it a
>>> shot.
>>>
>>> thx
>>>
>>>
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