We propose a linear-mapping based variational Ensemble Kalman filter for problems with generic observation models. Specifically, the proposed method is formulated as to construct a linear mapping from the prior ensemble to the posterior one, and the linear mapping is computed by minimizing the Kullback-Leibler divergence between the transformed distribution by the linear mapping and the actual posterior.
翻译:我们为通用观测模型的问题提出了一个基于线性图谱的基于线性图解的宽度图解的Kalman过滤器。 具体地说,拟议方法旨在构建从先前的组合图到后方图的线性图谱,而线性图谱的计算方法是将线性图谱图与实际的后方图谱的变形分布之间的 Kullback- Leiber 差异最小化。