文章基本信息

标题：Optimal Sensing Precision in Ensemble and Unscented Kalman Filtering ⁎
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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：5016-5021
DOI：10.1016/j.ifacol.2020.12.1101
语种：English
出版社：Elsevier
摘要：AbstractWe consider the problem of selecting an optimal set of sensor precisions to estimate the states of a non-linear dynamical system using an Ensemble Kalman filter and an Unscented Kalman filter, which uses random and deterministic ensembles respectively. Specifically, the goal is to choose at run-time, a sparse set of sensor precisions for active-sensing that satisfies certain constraints on the estimated state covariance. In this paper, we show that this sensor precision selection problem is a semidefinite programming problem when we usel1norm over precision vector as the surrogate measure to induce sparsity. We formulate a sensor selection scheme over multiple time steps, for certain constraints on the terminal estimated state covariance.
关键词：KeywordsNon-linear systemsestimationmonitoringoptimal sensingoptimization