Ensemble Consider Kalman Filtering

被引:0
|
作者
Lou, Tai-shan [1 ]
Chen, Nan-hua [1 ]
Xiong, Hua [2 ]
Li, Ya-xi [1 ]
Wang, Lei [3 ]
机构
[1] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
[2] Beijing Inst Elect Syst Engn, Beijing 100854, Peoples R China
[3] Beihang Univ, Sch Automat Sci & Elect Engn, Beijing 100083, Peoples R China
来源
2018 IEEE CSAA GUIDANCE, NAVIGATION AND CONTROL CONFERENCE (CGNCC) | 2018年
关键词
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:5
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