文章基本信息

标题：Eigen Value Analysis in Lower Bounding Uncertainty of Kalman Filter Estimates ⁎
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作者：Niladri Das ; Raktim Bhattacharya
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：5022-5027
DOI：10.1016/j.ifacol.2020.12.1103
语种：English
出版社：Elsevier
摘要：AbstractIn this paper we are concerned with the error-covariance lower-bounding problem in Kalman filtering: a sensor releases a set of measurements to the data fusion/estimation center, which has a perfect knowledge of the dynamic model, to allow it to estimate the states, while preventing it to estimate the states beyond a given accuracy. We propose a measurement noise manipulation scheme to ensure lower-bound on the estimation accuracy of states. Our proposed method ensures lower-bound on the steady state estimation error of Kalman filter, using mathematical tools from eigen value analysis.
关键词：KeywordsNon-linear systemsestimationmonitoringlower-boundoptimizationprivacyeigen value analysis