文章基本信息

标题：On the resilience of a class of Correntropy-based state estimators
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作者：Alexandre Kircher ; Laurent Bako ; Éric Blanco 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：2286-2291
DOI：10.1016/j.ifacol.2020.12.017
语种：English
出版社：Elsevier
摘要：AbstractThis paper deals with the analysis of a class of offline state estimators for LTI discrete-time systems in the presence of an arbitrary measurement noise which can potentially take any value. The considered class of estimators is defined as the solution of an optimization problem involving a performance function which can be interpreted as a generalization of cost functions used in the Maximum Correntropy Criterion. The conclusion of the analysis is that if the system is observable enough, then the considered class of estimators is resilient, which means that the obtained estimation error is independent from the highest values of the measurement noise. In the case of systems with a bounded process noise, the considered class of estimators provides a bounded estimation error under the appropriate conditions despite not being designed for this scenario.
关键词：KeywordsSecure state estimationMaximum Correntropy Criterion (MCC)optimal estimationCyber-physical systems