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arXiv 1509.05967eess.SYcs.SY
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协同H无穷型估计的分布式滤波器设计

Distributed Filter Design for Cooperative H-Infinity-Type Estimation

University of Stuttgart(斯图加特大学);University of Technology, Sydney(悉尼科技大学);University of New South Wales at the Australian Defence Force Academy(澳大利亚国防军学院新南威尔士大学)

Jingbo Wu, Li Li, Valery Ugrinovskii, Frank Allgöwer

AI总结:

本文研究了基于耦合线性矩阵不等式的设计问题,采用对偶方法高效实现分布式鲁棒滤波,本地估计器可局部迭代计算滤波增益,通信仅限于邻居。

摘要:

本文考虑了分布式鲁棒滤波问题,其中估计器设计基于一组耦合的线性矩阵不等式(LMIs)。我们将问题分解并证明多变量方法可以高效且去中心化地获得解决方案,即所有本地估计器可以局部和迭代地计算其滤波增益,通信仅限于邻居。迭代算法的收敛性质被分析和解释。

英文摘要:

In this paper, we consider the distributed robust filtering problem, where estimator design is based on a set of coupled linear matrix inequalities (LMIs). We separate the problem and show that the method of multipliers can be applied to obtain a solution efficiently and in a decentralized fashion, i.e. all local estimators can compute their filter gains locally and iteratively, with communications restricted to their neighbours. The convergence properties of the iterative algorithm are analyzed and interpreted.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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