Nonlinear non-Gaussian system filtering based on Gaussian sum and divided difference filter

Based on analyzing divided difference filter(DDF) and Gaussian sum filter(GSF),a GSF-based DDF algorithm is developed for nonlinear dynamic state space(DSS) models with non-Gaussian noise,which is suitable for the filtering problem of nonlinear/non-Gaussian systems.When the likelihood function appeares at the tail of the transfer probability density,the proposed algorithm can improve the precision of nonlinear/non-Gaussian filtering compared with the traditional particle filter(PF).Experiments show that the proposed method works well in the filtering for DSS models with non-Gaussian noise.