Constrained dual ensemble Kalman filter for state and parameter estimation

Abstract

The performance of a state estimator is dependent on the accuracy of the process model used. Since processes undergo various changes as time progresses, it is essential to adapt the model parameters to reflect the change in process conditions and maintain the accuracy of the model predictions. In several cases, it may be necessary to account for the physical bounds on the states and parameters while computing their estimates. In this work, a constrained dual ensemble Kalman filter (C-EnKF) for state and parameter estimation is proposed to construct the state and parameter estimates that are consistent with their physical limits. The efficacy of the proposed dual C-EnKF is demonstrated on two simulation case studies. The results obtained demonstrate that the proposed approach tracks parameter changes with reasonable accuracy, while maintaining the state and parameter estimates within their physical limits.