Gaussian sum filter of Markov jump non-linear systems

This paper proposes a Gaussian sum filtering (GSF) framework for the state estimation of Markov jump nonlinear systems. Through presenting the Gaussian sum approximations about the model-conditioned state posterior probability density functions, a general GSF framework in the minimum mean square error sense is derived. The minor Gaussian-set design is utilised to merge the Gaussian components at the beginning, which can effectively limit the computational requirements. Simulation result shows that the proposed algorithm demonstrates comparable performance to the interacting multiple model particle filter with significantly reduced computational cost.