Adaptive particle routing in parallel/distributed particle filters

Particle filters estimate the state of dynamic systems through Bayesian interference and stochastic sampling techniques. Parallel/distributed particle filters aim to improve the performance by deploying all particles on different processing units. However, the communication cost of transferring particles is high due to the centralized processing in resampling step. To reduce the communication cost without loss of accuracy, the hybrid particle routing policy is designed for the resampling step, which mainly executes particles resampling and exchanges locally and routes them globally every specific number of calculation steps. However, the global particle routing is more necessary when the convergence of particles is low. In this paper, we propose the adaptive particle routing algorithm, in which the local resampling and particle exchange are used, and the planned global particle routing is adopted only when the measured convergence is below the set threshold. The experimental results show the improved performance.