Recent Developments in Distributed Particle Filtering: Towards Fast and Accurate Algorithms

Particle filters have been widely used for the solution of optimal estimation problems in nonlinear non-Gaussian environments. One of their drawbacks is that these methods are computationally expensive. In the past few years, new developments have been made in trying to distribute the particle filter algorithm among different computing agents in order to make the underlying computations tractable. This period also witnessed the rise of general purpose GPU devices, which are making massive code parallelization possible. These developments have the potential to make the particle filter a viable alternative for real-time implementations in the near future, even when the number of required particles is high. In this paper we review the state-of-the-art in distributed particle filtering and propose a method that is applicable to distributed computing architectures.