For the nonlinear systems, the ensemble Kalman filter can avoid using the Jacobian matrices and reduce the computational complexity. However, the state estimates still suffer greatly negative effects from uncertain parameters of the dynamic and measurement models. To mitigate the negative effects, an ensemble consider Kalman filter (EnCKF) is designed by using the “consider” approach and resampling the ensemble members in each step to incorporate the statistics of the uncertain parameters into the state estimation formulations. The effectiveness of the proposed EnCKF is verified by two numerical simulations.
Ensemble Consider Kalman Filtering*
2018-08-01
222687 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Norm-Constrained Consider Kalman Filtering
AIAA | 2014
|AIAA | 2010
|Wiley | 2007
|Recursive Implementations of the Schmidt-Kalman ‘Consider’ Filter
Online Contents | 2013
|Recursive Implementations of the Schmidt-Kalman ‘Consider’ Filter
Springer Verlag | 2013
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