文章基本信息

标题：Optimal Transport Based Filtering with Nonlinear State Equality Constraints ⁎
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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：2373-2378
DOI：10.1016/j.ifacol.2020.12.035
语种：English
出版社：Elsevier
摘要：AbstractIn this work we propose a framework to address the issue of state dependent nonlinear equality-constrained state estimation using Bayesian filtering. This framework is constructed specifically for a linear approximation of Bayesian filtering that uses the theory of Optimal Transport. As a part of this framework, we present three traditionally-used nonlinear equality constraint-preserving algorithms coupled with the Optimal Transport based filter: the equality-constrained Optimal Transport filter, the projected Optimal Transport filter, and the measurement-augmented Optimal Transport filter. In cases where the nonlinear equality-constraints represent an arbitrary convex manifold, we show that the re-sampling step of Optimal Transport filter, can generate initial samples for filtering, from any probability distribution function defined on this manifold. We show numerical results using our proposed framework.
关键词：KeywordsNon-linear systemsestimationmonitoringsamplingoptimization