[ZZII 527]

Originally Posted by Salik Syed

If anyone is interested here is an excellent (and much easier to understand) paper on kalmann filtering :
http://www.cs.unc.edu/~welch/media/pdf/maybeck_ch1.pdf

I've seen that chapter referenced before so I'm glad someone posted it. I'll have to do more than just skim through it at some point, but I have a question:

The Kalman filter seems to deal with noise - specifically Gaussian white noise. That is, given two estimates of a state, say position, and a general idea of how noisy the estimates are, it will yield an optimal combination with less error than either of the two estimates alone. How does this apply to drift? Drift would seem to violate the Gaussian noise assumption, since the mean estimation error (not zero), integrated in time, is what causes the drift in the first place. That is, you could have a very clean signal that has still drifted away from the true position due to the integration. Wouldn't you still need a zero reference at some point?

I like the idea of using the accelerometer to signal a zero-reference update. It can tell when you are not spinning, so why not recalibrate the gyroscope to zero velocity then. Of course, that doesn't help with position directly. Also consider a vertical example: Say you want to cancel drift from the gyro on a Segway's primary axis. With a two-axis accelerometer oriented so that the third axis is the gyro axis, you can be fairly certain of when you are not moving because the resultant acceleration from both axes will be just gravity. When you see this condition for more than some period of time, recalibrate the gyroscope based on the trig of the two accelerometer readings. I'm thinking you can also do this on the fly with some trickier math, but I'm not sure.