TY - GEN
T1 - Interacting Multiple Model Estimator with Output Reference Learning
AU - Li, Wenling
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 - A class of interacting multiple model (IMM) estimators are regarded as one kind of instrumental tool to estimate the state of jump Markov systems, in which the overall estimate only can be considered as output. In this paper, the overall estimate is used to design output reference learning terms in the IMM estimator and they are utilized to update the mode-conditioned estimates recursively. Finally, simulations are presented to testify the validity of proposed estimator.
AB - A class of interacting multiple model (IMM) estimators are regarded as one kind of instrumental tool to estimate the state of jump Markov systems, in which the overall estimate only can be considered as output. In this paper, the overall estimate is used to design output reference learning terms in the IMM estimator and they are utilized to update the mode-conditioned estimates recursively. Finally, simulations are presented to testify the validity of proposed estimator.
KW - Interacting multiple model
KW - Jump Markov system
KW - Output reference learning
UR - https://www.scopus.com/pages/publications/85120616591
U2 - 10.1007/978-981-15-8155-7_33
DO - 10.1007/978-981-15-8155-7_33
M3 - 会议稿件
AN - SCOPUS:85120616591
SN - 9789811581540
T3 - Lecture Notes in Electrical Engineering
SP - 405
EP - 415
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 -