CASIA OpenIR  > 机器人应用与理论组
A Novel Case of Practical Exponential Observer Using Extended Kalman Filter
Ji, Daxiong1; Deng, Zhi1; Li, Shuo2; Ma, Dongfang1; Wang, Tao1; Song, Wei1; Zhu, Shiqiang1; Wang, Zhi3; Pan, Hongjun4; Sharma, Sanjay5; Yang, Xu6
Source PublicationIEEE ACCESS
ISSN2169-3536
2018
Volume6Pages:58004-58011
Corresponding AuthorJi, Daxiong(jidaxiong@zju.edu.cn)
AbstractThis technical note presents a case of practical exponential observer using extended Kalman filter (EKF) independent of certain restrictions, such as online check and estimation error of initial state. Recursive state estimation is usually a challenge for discrete-time nonlinear system in terms of computation cost. EKF is attractive with its simplicity since it is considered as an exponential observer given the above restrictions. However, those restrictions are so mathematically complicated that EKF cannot be practical in estimation. A novel case for an exponential observer using EKF is proposed, which is independent of such restrictions. However, these restrictions are proved to be unnecessary in the case. The proposed case is illustrated by a navigation system scenario. The validity of the case is demonstrated by a numerical simulation experiment. The system is deterministic.
KeywordDiscrete-time nonlinear system extended Kalman filter exponential observer restrictions spectral norm
DOI10.1109/ACCESS.2018.2870491
WOS KeywordSTATE ESTIMATION ; COOPERATIVE NETWORKS ; NONLINEAR ESTIMATION ; SYSTEMS ; TRACKING
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[51679213] ; National Key Research and Development Program of China[2016YFC0300801] ; Fundamental Research Funds for the Central Universities ; U.K. Royal Society-International Exchanges 2017 Cost Share, China[IEC\NSFC\170405]
Funding OrganizationNational Natural Science Foundation of China ; National Key Research and Development Program of China ; Fundamental Research Funds for the Central Universities ; U.K. Royal Society-International Exchanges 2017 Cost Share, China
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000449002100001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22831
Collection机器人应用与理论组
Corresponding AuthorJi, Daxiong
Affiliation1.Zhejiang Univ, Ocean Coll, Key Lab Ocean Observat Imaging Testbed Zhejiang P, Zhoushan 316000, Peoples R China
2.Chinese Acad Sci, Shenyang Inst Automat, State Key Lab Robot, Shenyang 110000, Liaoning, Peoples R China
3.Zhejiang Univ, Coll Control Sci & Engn, Zhoushan 316000, Peoples R China
4.Zhejiang Ocean Univ, Sch Math Phys & Informat Sci, Zhoushan 316000, Peoples R China
5.Plymouth Univ, Sch Engn, Plymouth PL4 8AA, Devon, England
6.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Ji, Daxiong,Deng, Zhi,Li, Shuo,et al. A Novel Case of Practical Exponential Observer Using Extended Kalman Filter[J]. IEEE ACCESS,2018,6:58004-58011.
APA Ji, Daxiong.,Deng, Zhi.,Li, Shuo.,Ma, Dongfang.,Wang, Tao.,...&Yang, Xu.(2018).A Novel Case of Practical Exponential Observer Using Extended Kalman Filter.IEEE ACCESS,6,58004-58011.
MLA Ji, Daxiong,et al."A Novel Case of Practical Exponential Observer Using Extended Kalman Filter".IEEE ACCESS 6(2018):58004-58011.
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