Multiple–Model UKF/CKF State Estimation for Nonlinear Systems

Xiaodi Shi; Liping Yan; Yuanqing Xia; Bo Xiao

doi:10.1007/978-981-15-8155-7_7

Multiple–Model UKF/CKF State Estimation for Nonlinear Systems

Xiaodi Shi, Liping Yan^*, Yuanqing Xia, Bo Xiao

^*此作品的通讯作者

自动化学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020
编辑	Liang Yan, Haibin Duan, Xiang Yu
出版商	Springer Science and Business Media Deutschland GmbH
页	79-90
页数	12
ISBN（印刷版）	9789811581540
DOI	https://doi.org/10.1007/978-981-15-8155-7_7
出版状态	已出版 - 2022
活动	International Conference on Guidance, Navigation and Control, ICGNC 2020 - Tianjin, 中国期限: 23 10月 2020 → 25 10月 2020

出版系列

姓名	Lecture Notes in Electrical Engineering
卷	644 LNEE
ISSN（印刷版）	1876-1100
ISSN（电子版）	1876-1119

会议

会议	International Conference on Guidance, Navigation and Control, ICGNC 2020
国家/地区	中国
市	Tianjin
时期	23/10/20 → 25/10/20

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10.1007/978-981-15-8155-7_7

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引用此

Shi, X., Yan, L., Xia, Y., & Xiao, B. (2022). Multiple–Model UKF/CKF State Estimation for Nonlinear Systems. 在 L. Yan, H. Duan, & X. Yu (编辑), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020 (页码 79-90). (Lecture Notes in Electrical Engineering; 卷 644 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-15-8155-7_7

Shi, Xiaodi ; Yan, Liping ; Xia, Yuanqing 等. / Multiple–Model UKF/CKF State Estimation for Nonlinear Systems. Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. 编辑 / Liang Yan ; Haibin Duan ; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. 页码 79-90 (Lecture Notes in Electrical Engineering).

@inproceedings{83ab2cf4acd144f3bd0d1b2f9e7f770f,

title = "Multiple–Model UKF/CKF State Estimation for Nonlinear Systems",

abstract = "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.",

keywords = "Multiple model CKF (MMCKF), Multiple model UKF (MMUKF), Probabilistic model design, Probabilistically weighted points",

author = "Xiaodi Shi and Liping Yan and Yuanqing Xia and Bo Xiao",

note = "Publisher Copyright: {\textcopyright} 2022, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; International Conference on Guidance, Navigation and Control, ICGNC 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2022",

doi = "10.1007/978-981-15-8155-7_7",

language = "English",

isbn = "9789811581540",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Liang Yan and Haibin Duan and Xiang Yu",

booktitle = "Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020",

address = "Germany",

}

Shi, X, Yan, L, Xia, Y & Xiao, B 2022, Multiple–Model UKF/CKF State Estimation for Nonlinear Systems. 在 L Yan, H Duan & X Yu (编辑), Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Lecture Notes in Electrical Engineering, 卷 644 LNEE, Springer Science and Business Media Deutschland GmbH, 页码 79-90, International Conference on Guidance, Navigation and Control, ICGNC 2020, Tianjin, 中国, 23/10/20. https://doi.org/10.1007/978-981-15-8155-7_7

Multiple–Model UKF/CKF State Estimation for Nonlinear Systems. / Shi, Xiaodi; Yan, Liping; Xia, Yuanqing 等.
Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. 编辑 / Liang Yan; Haibin Duan; Xiang Yu. Springer Science and Business Media Deutschland GmbH, 2022. 页码 79-90 (Lecture Notes in Electrical Engineering; 卷 644 LNEE).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Multiple–Model UKF/CKF State Estimation for Nonlinear Systems

AU - Shi, Xiaodi

AU - Yan, Liping

AU - Xia, Yuanqing

AU - Xiao, Bo

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

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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 -

Shi X, Yan L, Xia Y, Xiao B. Multiple–Model UKF/CKF State Estimation for Nonlinear Systems. 在 Yan L, Duan H, Yu X, 编辑, Advances in Guidance, Navigation and Control - Proceedings of 2020 International Conference on Guidance, Navigation and Control, ICGNC 2020. Springer Science and Business Media Deutschland GmbH. 2022. 页码 79-90. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-15-8155-7_7

Multiple–Model UKF/CKF State Estimation for Nonlinear Systems

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