I sounds like you really only need a state estimator. A kalman filter handles things like variable uncertainty (which is essential for things like GPS). It takes quite a lot of computing power (you have to invert a matrix, with is N^3) and is complicated to get right (some corner cases can be unstable).
If your system error is constant, then a state estimator is all you need. You just need a model of the system (probably just a second order physics model), and an estimate of the noise (probably hand tuned). Any controls book on state space controls should cover state estimators. -Dan On Wed, Aug 17, 2011 at 9:34 PM, Dave <[email protected]> wrote: ... ------------------------------------------------------------------------------ Get a FREE DOWNLOAD! and learn more about uberSVN rich system, user administration capabilities and model configuration. Take the hassle out of deploying and managing Subversion and the tools developers use with it. http://p.sf.net/sfu/wandisco-d2d-2 _______________________________________________ Emc-users mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/emc-users
