Quote:
Originally Posted by Paul Copioli
You need a complete understanding, mathematically, of your physical system for a Kalman filter to be effective..
|
This is false. Simplified models of your system can be used in effective Kalman filters provided you add uncertainty from the model.
See this paper which is an overview of the Kalman filter (esp. the section on Filter Parameters and Tuning and the Extended Kalman filter):
http://www.cs.unc.edu/~welch/media/pdf/kalman_intro.pdf
Quote:
|
Sometimes a relatively simple (poor) process model can produce acceptable results if one “injects” enough uncertainty into the process via the selection of Q [process noise covariance].
|
That being said, I agree that Kalman filters are generally overkill for FRC.