@inproceedings{c8c3344c8d7648f197eefe6a3261ed33,
title = "Ensemble Consider Kalman Filtering",
abstract = "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.",
author = "Lou, \{Tai Shan\} and Chen, \{Nan Hua\} and Hua Xiong and Li, \{Ya Xi\} and Lei Wang",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018 ; Conference date: 10-08-2018 Through 12-08-2018",
year = "2018",
month = aug,
doi = "10.1109/GNCC42960.2018.9018907",
language = "英语",
series = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE CSAA Guidance, Navigation and Control Conference, CGNCC 2018",
address = "美国",
}