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 <[email protected]> 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-kalman-filter/ > > 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+ > > > > > > _______________________________________________ > [email protected] mailing list > UNSUBSCRIBE and account-management -> > http://lists.puredata.info/listinfo/pd-list _______________________________________________ [email protected] mailing list UNSUBSCRIBE and account-management -> http://lists.puredata.info/listinfo/pd-list
