A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter
A Stochastic Covariance Shrinkage Approach to Particle Rejuvenation in the Ensemble Transform Particle Filter
复制标题
DOI：
10.5194/npg-29-241-2022
复制
发表时间：
2021-09
期刊：
影响因子：
--
通讯作者：
A. Popov;Amit N. Subrahmanya;Adrian Sandu
中科院分区：
文献类型：
--
作者：
A. Popov;Amit N. Subrahmanya;Adrian Sandu
摘要。当粒子数量不足以对状态空间的高概率区域进行采样时，为了防止权值崩溃，必须对粒子滤波器进行恢复。复壮通常是通过增加随机样本的启发式方式来实现的，这些随机样本扩大了整体的支持。这项工作旨在通过引入从气候样本中获得的额外先验信息来改进规范复兴方法；用随机协方差收缩得到的样本扩充用于重要抽样的动态粒子。该方法扩展了系综输运粒子滤波器及其二阶变体。数值实验表明，改进的滤波器显著改善了低动力系综尺寸的分析。
Abstract. Rejuvenation in particle filters is necessary to prevent the collapse of the weights when the number of particles is insufficient to sample the high probability regions of the state space. Rejuvenation is often implemented in a heuristic manner by the addition of stochastic samples that widen the support of the ensemble. This work aims at improving canonical rejuvenation methodology by the introduction of additional prior information obtained from climatological samples; the dynamical particles used for importance sampling are augmented with samples obtained from stochastic covariance shrinkage. The ensemble transport particle filter, and its second order variant, are extended with the proposed rejuvenation approach. Numerical experiments show that modified filters significantly improve the analyses for low dynamical ensemble sizes.