@inproceedings{3a6a0527d4e74efca8a979b28eff178b,
title = "Distributed implementations of particle filters",
abstract = "Particle filtering has a great potential for solving highly nonlinear and non-Gaussian estimation problems, generally intractable within a standard linear Kalman filtering based framework. However; the implementation of particle filters (PFs) is rather computationally involved, which nowadays prevents them from practical real-world application. A natural idea to make PFs feasible for {"}real-time{"} data processing is IO implement them on distributed multiprocessor computer systems. This paper presents three schemes for distributing the computations of generic particle filters, including resampling and. optionally, a Metropolis-Hastings (MH) step. Simulation results based on a maneuvering target tracking scenario show that distributed implementations can provide a promising solution 10 the steep computational burden incurred when using a large number of particles.",
keywords = "Distributed computing, Particle filter, Target tracking",
author = "Bashi, \{Anwer S.\} and Jilkov, \{Vesselin P.\} and \{Rong Li\}, X. and Huimin Chen",
year = "2003",
doi = "10.1109/ICIF.2003.177369",
language = "英语",
isbn = "0972184449",
series = "Proceedings of the 6th International Conference on Information Fusion, FUSION 2003",
publisher = "IEEE Computer Society",
pages = "1164--1171",
booktitle = "Proceedings of the 6th International Conference on Information Fusion, FUSION 2003",
note = "6th International Conference on Information Fusion, FUSION 2003 ; Conference date: 08-07-2003 Through 11-07-2003",
}