hello,
i had a look time ago about kalman (1D). from what i remember, this filter is
useful if you can model the input signal. if you can't, and use it as a generic
filter, then it is not better than a simple 1 pole filter.
in 2D (or more), it should be not very different than a 2D mass-spring network.
this remind me that i have to update physical model filter in mapping and
puremapping libs...
cheers
C
Le 17/12/2012 19:17, Pedro Lopes a écrit :
Got it working, but just realized that some of you need a 1D kalman,
and I was working on a 2D kalman. A cool version could accept params
[kalman <dim> <other params>]
Nothing works within pd yet, but lets see if I have time for that. I
will also play around with some 1D implementations.
best,
p
On Fri, Dec 14, 2012 at 2:06 PM, katja <[email protected]> wrote:
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 <[email protected]> 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 <[email protected]> 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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