A Hybrid Resampling Technique with Adaptive Intervals Used in the Parallel/Distributed Particle Filters
Authors: 
Xudong Zhang, Feng Gu, Wei Zhong, Chunyu AiAuthors Info & Claims
Pages 319 - 325
Abstract
Parallel/Distributed particle filters estimate the states of dynamic systems by using Bayesian interference and stochastic sampling techniques with multiple processing units (PUs). The sampling procedure and the resampling procedure alternatively execute to estimate the states in particle filters. There are two basic types of resampling techniques used in parallel/distributed particle filters. They are centralized resampling and decentralized resampling. The high communication between PUs in centralized resampling lowers the speedup factor in parallel computing but improves the estimation accuracy. The decentralized resampling can avoid the communication and improve the performance. Some types of hybrid resampling techniques mainly execute the decentralized resampling and only invoke the centralized resampling with constant intervals to achieve ideal performance without losing the estimation accuracy. However, the constant intervals cannot guarantee that the centralized resamplings are invoked timely. In this study, we proposed a hybrid resampling technique with adaptive intervals between centralized resamplings to overcome that issue. The experimental results indicate that the proposed hybrid resampling technique is able to improve the performance and the estimation accuracy.
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Published In
December 2023
563 pages
ISBN:9798400708688
DOI:10.1145/3638584
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Published: 14 March 2024
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- the Office of the Vice President for Research at the University of South Carolina
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CSAI 2023
CSAI 2023: 2023 7th International Conference on Computer Science and Artificial Intelligence
December 8 - 10, 2023
Beijing, China
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