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Penalized ensemble Kalman filters for high dimensional non-linear systems.

التفاصيل البيبلوغرافية
العنوان: Penalized ensemble Kalman filters for high dimensional non-linear systems.
المؤلفون: Elizabeth Hou, Earl Lawrence, Alfred O Hero
المصدر: PLoS ONE, Vol 16, Iss 3, p e0248046 (2021)
بيانات النشر: Public Library of Science (PLoS), 2021.
سنة النشر: 2021
المجموعة: LCC:Medicine
LCC:Science
مصطلحات موضوعية: Medicine, Science
الوصف: The ensemble Kalman filter (EnKF) is a data assimilation technique that uses an ensemble of models, updated with data, to track the time evolution of a usually non-linear system. It does so by using an empirical approximation to the well-known Kalman filter. However, its performance can suffer when the ensemble size is smaller than the state space, as is often necessary for computationally burdensome models. This scenario means that the empirical estimate of the state covariance is not full rank and possibly quite noisy. To solve this problem in this high dimensional regime, we propose a computationally fast and easy to implement algorithm called the penalized ensemble Kalman filter (PEnKF). Under certain conditions, it can be theoretically proven that the PEnKF will be accurate (the estimation error will converge to zero) despite having fewer ensemble members than state dimensions. Further, as contrasted to localization methods, the proposed approach learns the covariance structure associated with the dynamical system. These theoretical results are supported with simulations of several non-linear and high dimensional systems.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1932-6203
Relation: https://doaj.org/toc/1932-6203
DOI: 10.1371/journal.pone.0248046
URL الوصول: https://doaj.org/article/4a3dae8e8db841369f9e7ecafd710aa6
رقم الأكسشن: edsdoj.4a3dae8e8db841369f9e7ecafd710aa6
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:19326203
DOI:10.1371/journal.pone.0248046