Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters
Type
Conference PaperKAUST Department
Earth Fluid Modeling and Prediction GroupEarth Science and Engineering Program
Physical Science and Engineering (PSE) Division
Date
2010-09-20Online Publication Date
2010-09-20Print Publication Date
2010Permanent link to this record
http://hdl.handle.net/10754/552770
Metadata
Show full item recordAbstract
Optimal nonlinear filtering consists of sequentially determining the conditional probability distribution functions (pdf) of the system state, given the information of the dynamical and measurement processes and the previous measurements. Once the pdfs are obtained, one can determine different estimates, for instance, the minimum variance estimate, or the maximum a posteriori estimate, of the system state. It can be shown that, many filters, including the Kalman filter (KF) and the particle filter (PF), can be derived based on this sequential Bayesian estimation framework. In this contribution, we present a Gaussian mixture-based framework, called the particle Kalman filter (PKF), and discuss how the different EnKF methods can be derived as simplified variants of the PKF. We also discuss approaches to reducing the computational burden of the PKF in order to make it suitable for complex geosciences applications. We use the strongly nonlinear Lorenz-96 model to illustrate the performance of the PKF.Citation
Particle Kalman Filtering: A Nonlinear Framework for Ensemble Kalman Filters, AIP Conference Proceedings 1281 , 1075 (2010); doi: 10.1063/1.3497823Publisher
AIP PublishingConference/Event name
International Conference on Numerical Analysis and Applied Mathematics 2010, ICNAAM-2010ae974a485f413a2113503eed53cd6c53
10.1063/1.3497823