Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators

Håkon Hoel, Gaukhar Shaimerdenova, Raul Tempone

Research output: Contribution to journalArticlepeer-review

Abstract

We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.
Original languageEnglish (US)
Pages (from-to)351-390
Number of pages40
JournalFoundations of Data Science
Volume2
Issue number4
DOIs
StatePublished - Dec 2020

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