Density assisted particle filters for state and parameter estimation

In recent years the theory of particle filtering has continued to advance, and it has found increasing use in sequential signal processing. A weakness of particle filtering is that it is inadequate for problems that besides tracking of evolving states require the estimation of constant parameters. In this paper, we propose particle filters that do not have this limitation. We call these filters density assisted particle filters, of which special cases are the recently introduced Gaussian particle filters and Gaussian sum particle filters. An implementation of a density particle filter is shown on a relatively simple but important nonlinear model. Simulations are included that show the performance of this filter.