浅谈包络卡尔曼滤波
A Brief Tutorial on the Ensemble Kalman Filter
摘要：
集合卡尔曼滤波（EnKF）是一种递归滤波器，适用于具有大量变量的问题，例如地球物理模型中偏微分方程的离散化。EnKF起源于卡尔曼滤波器的一个版本，用于解决大型问题（本质上，协方差矩阵被样本协方差取代），现在它是集合预报的重要资料同化组件。EnKF与粒子滤波器有关（在这种情况下，粒子与系综成员是同一回事），但EnKF假设所有涉及的概率分布都是高斯分布。本文简要介绍了EnKF的基本版本的推导和实际实现，并回顾了几个扩展。
英文摘要：
The ensemble Kalman filter (EnKF) is a recursive filter suitable for problems with a large number of variables, such as discretizations of partial differential equations in geophysical models. The EnKF originated as a version of the Kalman filter for large problems (essentially, the covariance matrix is replaced by the sample covariance), and it is now an important data assimilation component of ensemble forecasting. EnKF is related to the particle filter (in this context, a particle is the same thing as an ensemble member) but the EnKF makes the assumption that all probability distributions involved are Gaussian. This article briefly describes the derivation and practical implementation of the basic version of EnKF, and reviews several extensions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
