Particle filtering in high-dimensional systems with Gaussian approximations

In this paper we introduce a new multiple particle filtering approach for problems where the state-space of the system is of high-dimension. We propose to break the space into subspaces and to perform separate particle filtering in each of them. The two critical operations of particle filtering, the particle propagation and weight computation of each particle filter are performed wherever necessary with the aid of parametric distributions received from other subspaces. The proposed method is demonstrated by computer simulations and the results show an excellent performance when compared to other implementations of multiple particle filtering.