文章基本信息

标题：Optimization Based Constrained Gaussian Sum Unscented Kalman Filter ∗
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作者：Krishna Kumar Kottakki ; Mani Bhushan ; Sharad Bhartiya 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2016
卷号：49
期号：1
页码：59-64
DOI：10.1016/j.ifacol.2016.03.029
语种：English
出版社：Elsevier
摘要：AbstractThis work presents a novel constrained nonlinear state estimation approach for nonlinear dynamical systems. The proposed approach combines two key elements from well know Gaussian Sum Unscented Kalman Filter (GS-UKF) and Unscented Recursive Nonlinear Dynamic Data Reconciliation (URNDDR) approaches. The proposed approach uses sum of Gaussians representation in GS-UKF and explicit constrained update in URNDDR to obtain feasible state estimates. The benefits of the proposed approach are demonstrated over the available constrained GS-UKF variants using a three state isothermal batch process case study available in literature.
关键词：KeywordsSum of Gaussiansnonlinear state estimationinterval constraintsconstrained updateBayes’ ruleKalman Filter