Nonlinear Gaussian filtering via radial basis function approximation

Huazhen Fang, Jia Wang, Raymond A. de Callafon
2012 2012 IEEE 51st IEEE Conference on Decision and Control (CDC)  
This paper presents a novel type of Gaussian filter -the radial basis Gaussian filter (RB-GF) -for nonlinear state estimation. In the RB-GF, we propose to use radial basis functions (RBFs) to approximate the nonlinear process and measurement functions of a system, considering the superior approximation performance of RBFs. Optimal determination of the approximators is achieved by RBF neural network (RBFNN) learning. Using the RBF based function approximation, the challenging problem of integral
more » ... evaluation in Gaussian filtering can be well solved, guaranteeing the filtering performance of the RB-GF. The proposed filter is studied through numerical simulation, in which a comparison with other existing methods validates its effectiveness. H. Fang is with the
doi:10.1109/cdc.2012.6425941 dblp:conf/cdc/FangWC12 fatcat:gvzff5i3bnfxzaepvlbgwrc7sa