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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 integraldoi:10.1109/cdc.2012.6425941 dblp:conf/cdc/FangWC12 fatcat:gvzff5i3bnfxzaepvlbgwrc7sa