A novel hybrid resampling algorithm for parallel/distributed particle filters

Parallel/Distributed particle filters have been widely used in the estimation of states of dynamic systems by using multiple processing units (PUs). In parallel/distributed particle filters, the centralized resampling needs a central unit (CU) to serve as a hub to execute the global resampling. The centralized scheme is the main obstacle for the improved performance due to its global nature. To reduce the communication cost, the decentralized resampling was proposed, which only conducted the resampling on each PU. Although the decentralized resampling can improve the performance, it suffers from the low accuracy due to the local nature. Therefore, we propose a novel hybrid resampling algorithm to dynamically adjust the intervals between the centralized resampling steps and the decentralized resampling steps based on the measured system convergence. We formulate the proposed algorithm and prove it to be uniformly convergent. Since the proposed algorithm is a generalization of various versions of the hybrid resampling, its proof provides the solid theoretical foundation for their wide adoptions in parallel/distributed particle filters. In the experiments, we evaluate and compare different resampling algorithms including the centralized resampling algorithm, the decentralized resampling algorithm, and different types of existing hybrid resampling algorithms to show the effectiveness and the improved performance of the proposed hybrid resampling algorithm. (C) 2021 Elsevier Inc. All rights reserved.