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分布式网络信息共享Flooding的收敛性和分布式Bayes滤波应用

已有 8120 次阅读 2017-1-20 20:27 |个人分类:科研笔记|系统分类:论文交流| 网络, 一致性, 跟踪, Flooding, 分布式滤波

越是基础的东西越是重要,越是简单明了的东西有时候更实用 - 虽然不那么学术,玩起来不够酷不够炫。 Flooding信息分享就是这么一个技术!学术上被忽略了。非常有趣的“定义 - 定理 - 证明”科学官科八股文。

另外,Flooding这个词怎么翻译为中文合适呐?

Convergence of Distributed Flooding and Its Application for Distributed Bayesian Filtering

Abstract:

Distributed flooding is a fundamental information sharing method to obtaining network consensus via peer-to-peer communication. However, a unified consensus-oriented formulation of the algorithm and its convergence performance are not explicitly available in the literature. To fill this void in this paper, set-theoretic flooding rules are defined by encapsulating the information of interest in finite sets (one set per node), namely distributed set-theoretic information flooding (DSIF). This leads to a new type of consensus called "collecting consensus" which aims to ensure that all nodes get the same information. Convergence and optimality analyses are provided based on a consistent measure of the degree of consensus (DoC) of the network. Compared with the prevailing averaging consensus, the proposed DSIF protocol benefits from avoiding repeated use of any information and offering the highest converging efficiency for network consensus while being exposed to increasing node-storage requirements against communication iterations and higher communication load. The protocol has been advocated for distributed nonlinear Bayesian filtering, where each node operates a separate particle filter, and the collecting consensus is sought on the sensor data alone or jointly with intermediate local estimates. Simulations are provided to demonstrate the theoretical findings.




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