TY - GEN
T1 - Multiple–Model UKF/CKF State Estimation for Nonlinear Systems
AU - Shi, Xiaodi
AU - Yan, Liping
AU - Xia, Yuanqing
AU - Xiao, Bo
N1 - Publisher Copyright:
© 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In most control systems, modeling error and noise interference will always lead to the performance degradation and divergence of the UKF or the CKF. To settle a matter caused by model uncertainties, a new UKF/CKF frame combined with multiple model method is presented in this paper. Through probabilistic multiple model design method, this paper approximates the posterior densities by a finite number of probabilistically weighted points and uses these points to display the entire state space. Simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple-model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties.
AB - In most control systems, modeling error and noise interference will always lead to the performance degradation and divergence of the UKF or the CKF. To settle a matter caused by model uncertainties, a new UKF/CKF frame combined with multiple model method is presented in this paper. Through probabilistic multiple model design method, this paper approximates the posterior densities by a finite number of probabilistically weighted points and uses these points to display the entire state space. Simulation results and comparison analysis demonstrate that the multiple-model UKF(MMUKF) and the multiple-model CKF(MMCKF) have higher precision and stronger robustness than the traditional UKF and CKF in case of model uncertainties.
KW - Multiple model CKF (MMCKF)
KW - Multiple model UKF (MMUKF)
KW - Probabilistic model design
KW - Probabilistically weighted points
UR - http://www.scopus.com/inward/record.url?scp=85120649932&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-8155-7_7
DO - 10.1007/978-981-15-8155-7_7
M3 - Conference contribution
AN - SCOPUS:85120649932
SN - 9789811581540
T3 - Lecture Notes in Electrical Engineering
SP - 79
EP - 90
BT - Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Yu, Xiang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2020
Y2 - 23 October 2020 through 25 October 2020
ER -