Gaussian sum particle filtering for dynamic state space models

For dynamic systems, sequential Bayesian estimation requires updating of the filtering and predictive densities. For nonlinear and non-Gaussian models, sequential updating is not as straightforward as in the linear Gaussian model. In this paper, densities are approximated as finite mixture models as is done in the Gaussian sum filter. A novel method is presented, whereby sequential updating of the filtering and posterior densities is performed by particle based sampling methods. The filtering method has combined advantages of Gaussian sum and particle based filters and simulations show that the presented filter can outperform both methods.