Conference paper

Straka O., Flídr M., Duník J., Šimandl M.

Proceedings of the 15th IFAC Symposium on System Identification

Saint-Malo, France

2009

510-515

Ing. Miroslav Flídr, Ph.D. (FAV Západočeská univerzita, katedra kybernetiky)

nonlinear state and parameter estimation, Bayesian approach

clanek_SYSID09_MF.pdf [file]

poster_SYSID09_MF.pdf [file]

The goal of the article is to describe a software framework designed for nonlinear state estimation of discrete time dynamic systems. The framework was designed with the aim to facilitate implementation, testing and use of various nonlinear state estimation methods in mind. The main strength of the framework is its versatility due to the possibility of either structural or probabilistic description of the problem. Besides the well-known basic nonlinear estimation methods such as the extended Kalman filter, the divided difference filters and the unscented Kalman filter, the framework implements particle filter with advanced features as well. As the framework is designed on the object oriented basis, further extension by user-specified nonlinear estimation algorithms is extremely easy. The paper provides a brief introduction into nonlinear state estimation problem and describes the individual components of the framework, their key features and use. The strengths of the framework are presented in two examples.