Multiple Particle Filtering

Particle filtering is a sequential signal processing methodology that uses discrete random measures composed of particles and weights to approximate probability distributions of interest. The quality of approximation depends on many factors including the number of particles used for filtering and the way new particles are generated by the filter. The problem of good approximation becomes increasingly challenging as the dimension of the state space increases. In this paper, we address a possible solution for improved particle filtering in high dimensional cases by using a set of particle filters operating on partitioned subspaces of the complete state space. We provide simulation results that show the feasibility of the proposed approach.