Pedro, if you make the Kalman filter for Pd, I will try it for filtering barometer sensor data. I already tried an alpha-beta filter, but it produced overshoot and ring. A butterworth filter with coefficients taken from an open source variometer project performed better (but still not good enough):
https://github.com/lebipbip/le-BipBip
Katja
On Wed, Dec 12, 2012 at 4:37 AM, patrick puredata@11h11.com wrote:
hi,
i was trying to implement this filter in an avr, but i guess it would be even better in pd. i just need to clean a noisy accelerometer (only 1 axis), but then i read that for 1 input you can use a low pass (in comments):
http://interactive-matter.eu/blog/2009/12/18/filtering-sensor-data-with-a-ka...
as for now, i am using an iir filter in pd (from mapping). works great, but would like to "benchmark" the kalman implementation compared to low pass, iir.
anyone have a gem patch to plot mutiple inputs?
à+
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