Ensemble Consider Kalman Filtering

TL;DR

This paper introduces the ensemble consider Kalman filter (EnCKF), which effectively handles uncertain parameters in nonlinear models by integrating their statistics into state estimation, reducing computational complexity.

Contribution

The paper proposes the EnCKF that incorporates uncertain parameter statistics into the state estimation process using an augmented-state approach, avoiding Jacobian matrices.

Findings

EnCKF mitigates negative effects of uncertain parameters.

EnCKF reduces computational complexity.

Numerical simulations validate EnCKF effectiveness.

Abstract

In this paper, the ensemble consider Kalman filter is proposed to mitigate the negative effects of uncertain parameters in nonlinear dynamic and measurement models. The ensemble Kalman filter can avoid using the Jacobian matrices and reduce the computational complexity, the unknown parameters of the models still are not considered. By incorporating the statistics of the uncertain parameters into the state estimation formulations and using an augmented-state approach, the ensemble integration is reset by resampling the ensemble members in the new step, and the EnCKF algorithm is derived. Two numerical simulations show that the presented EnCKF can mitigate the negative effects of the uncertain parameters.