Sigma Point Gaussian Sum Filter Design using Square Root Unscented Filters

Authors:	Simandl Miroslav, University of West Bohemia in Pilsen, Czech Republic Dunik Jindrich, University of West Bohemia in Pilsen, Czech Republic
Topic:	1.1 Modelling, Identification & Signal Processing
Session:	Nonlinear Filtering
Keywords:	state estimation, estimation theory, filtering techniques, nonlinear filters, stochastic systems

Abstract

Local and global estimation approaches are discussed, above all the Unscented Kalman Filter and the Gaussian Sum Filter. The square root modification of the Unscented Kalman Filter is derived and it is used in the Gaussian Sum Filter framework. The new Sigma Point Gaussian Sum Filter is designed and some aspects of the filter are presented. Estimation quality and computational demands of the filter are illustrated in a numerical example.