Ensemble Consider Kalman Filtering

Tai-shan Lou; Nan-hua Chen; Hua Xiong; Ya-xi Li; Lei Wang

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arXiv · 1906.06695

Ensemble Consider Kalman Filtering

Tai-shan Lou·Nan-hua Chen·Hua Xiong·Ya-xi Li·Lei Wang

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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.

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Tai-shan Lou, Nan-hua Chen, Hua Xiong, Ya-xi Li, Lei Wang. 2019-06-16. Ensemble Consider Kalman Filtering. https://arxiv.org/abs/1906.06695

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