Authors:
Jindřich Havlík
;
Ondřej Straka
;
Jindřich Duník
and
Jiří Ajgl
Affiliation:
University of West Bohemia, Czech Republic
Keyword(s):
State Estimation, Bayesian Approach, Stochastic Integration, Measures of Nonlinearity, Measures of Non-Gaussianity.
Related
Ontology
Subjects/Areas/Topics:
Informatics in Control, Automation and Robotics
;
Nonlinear Signals and Systems
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Identification
Abstract:
The paper deals with Bayesian state estimation of nonlinear stochastic dynamic systems. The focus is aimed at the stochastic integration filter, which is based on a stochastic integration rule. It is shown that the covariance matrix of the integration error calculated as a byproduct of the rule can be used as a measure of nonlinearity. The measure informs the user about validity of the assumptions of Gaussianity, which is adopted by the stochastic integration filter. It is also demonstrated how to use this information for a prediction of the number of remaining iterations of the rule. The paper also focuses on utilization of the integration error covariance matrix for improving estimates of the mean square error of the estimates, which is produced by the filter.