Nonlinear Nonnormal Dynamic Models: State Estimation and Software
Miroslav Ŝimandl, Miroslav Flídr · Birkhäuser Boston eBooks · 1997
The purpose of this paper is to formulate and to solve recursive Bayesian state estimation problems for nonlinear non-Gaussian models and to introduce software for model and filter design. The initial a priori density of the state vector, state and measurement noise are always represented in the form of a discrete normal density mixture and a posteriori density of state vector has the same form. A software package was developed for solving Bayesian nonlinear filtering problems. The software package is based on MATLAB and the Symbolic Math Toolbox with an aim to create an easy-to-use tool for model and filter design. This tool can be used for state estimation of general nonlinear non-Gaussian systems or for many special problems like tracking time-varying parameters, fault detection, hypothesis testing, robustification of filters due to outliers and so on. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.