  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.
