Multiple particle filtering with improved efficiency and performance

Particle filtering has been widely accepted as an important methodology for processing data represented by state-space models characterized by nonlinearities and/or non-Gaussianities. It is also well documented that particle filtering deteriorates quickly in performance when the dimension of the tracked state becomes large. This limits its application in many science/engineering problems. Previously we have proposed a way of alleviating this deficiency based on the use of multiple particle filtering. According to the approach, a number of particle filters are assigned to track different subsets of the state with time. In this paper, we propose a new method for accurate and efficient implementation of multiple particle filtering. We provide simulation results that demonstrate the performance of the new method.