A localized ensemble Kalman-Bucy filter for nonlinear short-range models has component-wise mean-squared error of order sqrt(ε) independent of state dimension, with pathwise error growing only logarithmically in time.
On the continuous time limit of Ensemble Square Root Filters
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
We provide a continuous time limit analysis for the class of Ensemble Square Root Filter algorithms with deterministic model perturbations. In the particular linear case, we specify general conditions on the model perturbations implying convergence of the empirical mean and covariance matrix towards their respective counterparts of the Kalman-Bucy Filter. As a second main result we identify additional assumptions for the convergence of the whole ensemble towards solutions of the Ensemble Kalman-Bucy filtering equations introduced in [1]. The latter result can be generalized to nonlinear Lipschitz-continuous model operators. A striking implication of our results is the fact that the limiting equations for the ensemble members are universal for a large class of Ensemble Square Root Filters. This yields a mathematically rigorous justification for the analysis of these algorithms with the help of the Ensemble Kalman-Bucy Filter. [1] de Wiljes, Jana, Reich, Sebastian, Stannat, Wilhelm, Long-Time Stability and Accuracy of the Ensemble Kalman-Bucy Filter for Fully Observed Processes and Small Measurement Noise. SIAM Journal on Applied Dynamical Systems, Vol. 17, No. 2, 1152-1181, 2018
citation-role summary
citation-polarity summary
fields
math.NA 1years
2019 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations
A localized ensemble Kalman-Bucy filter for nonlinear short-range models has component-wise mean-squared error of order sqrt(ε) independent of state dimension, with pathwise error growing only logarithmically in time.