The last post covered the concept of the Exponentially Weighted Moving Average Filter and illustrated how it worked on a theoretical example, both with and without noise.  To wrap up, I want to include an actual set of data from the Devantech CMPS10 Tilt Compensated Compass on my current robot.  Although, as the name says, it has tilt compensation, I’m using the raw magnetometer output, as the robot is running on flat floors, and rotations and accelerations are liable to introduce more error than the practically non-existent tilt.

In my code, I’ve set α to a fairly high value of 0.33.  Here’s a plot of both the raw and filtered output for a case where the robot stayed fixed, then was manually rotated rather quickly to a new position:

As you can see form the plot, the filtered response, as expected, lags the raw response after the turn.  However, the raw output overshoots (I’m not sure why), so the lag actually results in the filtered output more closely matching reality, even right after the turn.  It’s not possible to see the effects of the filter on the noise from this plot, so this second graph shows just the raw and filtered data before the rotation, with the scale blown up:

The filter’s ability to modulate the noise is clearly evident.  The raw output bounces around +/- about 4.6 degrees, while the filtered output jumps within the much narrower range of +/- 1.5 degrees.