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. 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 the combined advantages of Gaussian sum and particle-based filters and simulations show that the presented filter can outperform both methods.