State and Parameter Estimation from Observed Signal Increments

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.

• Publication year

  2019

• Venue

  Entropy

• Publication date

  2019-03-26

• Fields of study

  Physics, Computer Science, Mathematics, Engineering, Medicine

• Identifiers
  DOI 10.3390/e21050505arXiv 1903.10717PMID 33267219PMCID 7514994
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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