TY  - JOUR
A1  - Nüsken, Nikolas
A1  - Reich, Sebastian
A1  - Rozdeba, Paul J.
T1  - State and parameter estimation from observed signal increments
T2  - Entropy : an international and interdisciplinary journal of entropy and information studies
N2  - The success of the ensemble Kalman filter has triggered a strong interest in expanding its scope beyond classical state estimation problems. In this paper, we focus on continuous-time data assimilation where the model and measurement errors are correlated and both states and parameters need to be identified. Such scenarios arise from noisy and partial observations of Lagrangian particles which move under a stochastic velocity field involving unknown parameters. We take an appropriate class of McKean-Vlasov equations as the starting point to derive ensemble Kalman-Bucy filter algorithms for combined state and parameter estimation. We demonstrate their performance through a series of increasingly complex multi-scale model systems.
KW  - parameter estimation
KW  - continuous-time data assimilation
KW  - ensemble Kalman filter
KW  - correlated noise
KW  - multi-scale diffusion processes
Y1  - 2019
UR  - https://publishup.uni-potsdam.de/frontdoor/index/index/docId/49606
SN  - 1099-4300
VL  - 21
IS  - 5
PB  - MDPI
CY  - Basel
ER  -