The robotics project I'm working on suffers from some very noisy sensors. A simple 1st order low pass filter doesn't do the trick. The noise has a low frequency cyclic component as well as standard white noise, plus occasional electrical spikes. It's not great.
We tried a number of things including 2nd order filters, bandpass filtering (to try and remove the low frequncy cycle), fuzzy sets and more.
I was hoping to avoid a Kalman filter style approch (since they are painfull to formulate, and they are only meant to handle gausian noise as far as I know). H-infinity filters are another option, (which can handle non-gausian noise and don't require a complete system model) but they are still too complex for my liking.
So we settled on trying an Alpha-Beta filter since that should give a vauge idea of how the Kalman might act, and its looking promising.
I'd never heard of these before, so thank you wikipedia! I'll have to spread the good word.
Stripping 3D Printed Gears for Science
2 hours ago