一种平滑化的集合卡尔曼滤波器

An ensemble Kalman filter approach based on level set parameterization for acoustic source identification using multiple frequency information

The spatial dependent unknown acoustic source is reconstructed according noisy multiple frequency data on a remote closed surface. Assume that the unknown function is supported on a bounded domain. To determine the support, we present a statistical inversion algorithm, which combines the ensemble Kalman filter approach with level set technique. Several numerical examples show that the proposed method give good numerical reconstruction.

Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations

Concurrent observation technologies have made high-precision real-time data available in large quantities. Data assimilation (DA) is concerned with how to combine this data with physical models to produce accurate predictions. For spatial-temporal models, the Ensemble Kalman Filter with proper localization techniques is considered to be a state-of-the-art DA methodology. This article proposes and investigates a localized Ensemble Kalman Bucy Filter (l-EnKBF) for nonlinear models with short-range interactions. We derive dimension-independent and component-wise error bounds and show the long tim…

ensemble 协同卡尔曼滤波崩溃告诉我们关于粒子滤波器什么

What the collapse of the ensemble Kalman filter tells us about particle filters

研究探讨了协同卡尔曼滤波崩溃与高维粒子滤波器的关系，指出粒子滤波器在高维问题中崩溃，支持气象学中对粒子滤波器进行局部化的努力。

An extended sampling-ensemble Kalman filter approach for partial data inverse elastic problems

Inverse problems are more challenging when only partial data are available in general. In this paper, we propose a two-step approach combining the extended sampling method and the ensemble Kalman filter to reconstruct an elastic rigid obstacle using partial data. In the first step, the approximate location of the unknown obstacle is obtained by the extended sampling method. In the second step, the ensemble Kalman filter is employed to reconstruct the shape. The location obtained in the first step guides the construction of the initial particles of the ensemble Kalman filter, which is critical …

平均场集合卡尔曼滤波器：近高斯设定

The Mean Field Ensemble Kalman Filter: Near-Gaussian Setting

本文首次分析了平均场集合卡尔曼滤波器在高斯设定之外的精度，通过引入加权全变差度量证明其稳定性，并证明在高斯问题邻域内误差很小。

混沌与机器学习动力系统集合卡尔曼滤波器的长时间精度

Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems

本文研究高维动力系统的在线状态估计问题，确立了集合卡尔曼滤波器在混沌系统及机器学习替代动力学模型中的长时间精度，证明了其估计误差在长时间范围内保持较小，验证了机器学习预测模型在集合数据同化中的有效性。