Repository: Freie Universität Berlin, Math Department

State and parameter estimation from observed signal increments

Nüsken, N. and Reich, S. and Rozdeba, P.J. (2019) State and parameter estimation from observed signal increments. Entropy, 21 (5). -505. ISSN 1099-4300

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Official URL: https://dx.doi.org/10.3390/e21050505

Abstract

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.

Item Type:Article
Additional Information:SFB 1114 Preprint in arXiv:1903.10717 (https://arxiv.org/abs/1903.10717)
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
ID Code:2348
Deposited By: Silvia Hoemke
Deposited On:06 Jun 2019 08:52
Last Modified:06 Jun 2019 12:24

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