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.

通过使用多个处理单元（PU），并行/分布式粒子滤波器已广泛用于估计动态系统的状态。在并行/分布式粒子滤波器中，集中式重采样需要一个中央单元（CU）作为执行全局重采样的集线器。集中式方案由于其全球性而成为提高性能的主要障碍。为了降低通信成本，提出了分散式重采样，其仅在每个PU上进行了重采样。尽管分散的重采样可以改善性能，但是由于局部性质，它存在精度低的问题。因此，我们提出了一种新颖的混合重采样算法，可以基于测得的系统收敛性来动态调整集中式重采样步骤和分散式重采样步骤之间的间隔。我们制定提出的算法，并证明它是一致收敛的。由于所提出的算法是混合重采样的各种版本的概括，因此其证明为它们在并行/分布式粒子滤波器中的广泛采用提供了坚实的理论基础。在实验中，我们评估并比较了不同的重采样算法，包括集中式重采样算法，分散式重采样算法以及不同类型的现有混合重采样算法，以证明所提出的混合重采样算法的有效性和改进的性能。它的证明为它们在并行/分布式粒子滤波器中的广泛采用提供了坚实的理论基础。在实验中，我们评估并比较了不同的重采样算法，包括集中式重采样算法，分散式重采样算法以及不同类型的现有混合重采样算法，以证明所提出的混合重采样算法的有效性和改进的性能。它的证明为它们在并行/分布式粒子滤波器中的广泛采用提供了坚实的理论基础。在实验中，我们评估并比较了不同的重采样算法，包括集中式重采样算法，分散式重采样算法以及不同类型的现有混合重采样算法，以证明所提出的混合重采样算法的有效性和改进的性能。