Recursive non-linear bayesian state estimation and model recognition with application to contouring tasks
Tine Lefebvre, Herman Bruyninckx, Joris De Schutter · 2004
This paper presents a new filter developed for exact nonlinear Bayesian state estimation and model recognition. The filter is able to deal (i) with static systems with any kind of nonlinear measurement function subject to additive Gaussian measurement uncertainty, and (ii) with a limited class of dynamic systems. Experimental position estimation and shape recognition results during a contouring task show the strength of the new nonlinear filtering approach.