@inproceedings{1ac05fc549f44ce9b18baab309b6f076,
title = "A New Robust Centered Error Entropy Cubature Kalman Filter",
abstract = "The heavy-tailed non-Gaussian noise often appears in the actual system and the classical cubature Kalman filter (CKF) algorithm will have reduced filtering accuracy or even filtering divergence in this condition. To make the CKF algorithm more robust, the centered error entropy cubature Kalman filter (CEECKF) algorithm is derived by combining the Spherical-Radial cubature rule and the centered error entropy (CEE) criterion. The proposed algorithm uses the cubature rule to obtain the one-step prediction state mean and covariance and then uses the CEE criterion to update the posterior state. The application in attitude determination shows the effectiveness of the algorithm.",
keywords = "centered error entropy, Cubature rule, heavy-tailed distribution, Kalman filter, non-Gaussian noise",
author = "Baojian Yang and Lu Cao and Lingwei Li and Chen Jiang and Dechao Ran and Bing Xiao",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021 ; Conference date: 30-07-2021 Through 01-08-2021",
year = "2021",
month = jul,
day = "30",
doi = "10.1109/ICCSSE52761.2021.9545184",
language = "英语",
series = "2021 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "119--124",
booktitle = "2021 7th International Conference on Control Science and Systems Engineering, ICCSSE 2021",
}