Ensemble Kalman methods with constraints
Ensemble Kalman methods with constraints
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DOI：
10.1088/1361-6420/ab1c09
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发表时间：
2019-09-01
期刊：
影响因子：
2.1
通讯作者：
Stuart, Andrew
中科院分区：
文献类型：
--
作者：
Albers, David J.;Blancquart, Paul-Adrien;Stuart, Andrew
在状态估计和参数估计问题中，集成卡尔曼方法已成为越来越重要的工具。它们的流行源于方法的无导数性质，当计算机代码可用于潜在的状态空间动力学（用于状态估计）或参数到可观察映射（用于参数估计）时，这种方法可以很容易地应用。在许多应用中，需要在状态或参数上以相等或不相等约束的形式强制执行先验信息。本文建立了这样做的一般框架，描述了一种广泛适用的方法，证明该方法的理论，以及一组数值实验。
Ensemble Kalman methods constitute an increasingly important tool in both state and parameter estimation problems. Their popularity stems from the derivative-free nature of the methodology which may be readily applied when computer code is available for the underlying state-space dynamics (for state estimation) or for the parameter-to-observable map (for parameter estimation). There are many applications in which it is desirable to enforce prior information in the form of equality or inequality constraints on the state or parameter. This paper establishes a general framework for doing so, describing a widely applicable methodology, a theory which justifies the methodology, and a set of numerical experiments exemplifying it.