文章基本信息

标题：Asymptotically Optimal Distributed Filtering of Continuous-Time Linear Systems
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作者：S. Battilotti ; F. Cacace ; M. d’Angelo 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：3242-3247
DOI：10.1016/j.ifacol.2020.12.1124
语种：English
出版社：Elsevier
摘要：AbstractIn this paper we prove the following new and unexpected result: it is possible to design a continuous-time distributed filter for linear systems that asymptotically tends at each node to the optimal centralized filter. The result concerns distributed estimation over a connected undirected graph and it only requires to exchange the estimates among adjacent nodes. We exhibit an algorithm containing a consensus term with a parametrized gain and show that when the parameter becomes arbitrarily large the error covariance at each node becomes arbitrarily close to the error covariance of the optimal centralized Kalman filter.
关键词：KeywordsContinuous time filtersKalman filtersFiltering theoryConsensus filters