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Adaptive square-root CKF with application to DR/LBL integrated heading estimation for HOV
Liu KZ(刘开周); Liu B(刘本); Wang YY(王艳艳); Zhao Y(赵洋); Cui SG(崔胜国); Feng XS(封锡盛)
Department水下机器人研究室
Conference Name27th Chinese Control and Decision Conference, CCDC 2015
Conference DateMay 23-25, 2015
Conference PlaceQingdao, China
Source PublicationProceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
PublisherIEEE
Publication PlacePiscataway, NJ, USA
2015
Pages1851-1855
Indexed ByEI ; CPCI(ISTP)
EI Accession number20154401482268
WOS IDWOS:000375232903035
Contribution Rank1
ISBN978-1-4799-7016-2
KeywordDr/lbl Heading Estimation Maximum a Posterior Human Occupied Vehicle Adaptive Square-root Cubature Kalman Filter
AbstractDead Reckoning (DR) and Long Base Line (LBL) are a modern method in navigation of Human Occupied Vehicles (HOV). However, the accuracy of DR system would degrade sharply, and due to the obvious error drifts of each unit involved in DR. LBL has the disadvantage of low update frequency. To improve the heading estimation of DR/LBL, this paper proposes an innovative method which could adjust state error variance matrix Q in real time dynamically. Square-root Cubature Kalman filter (SR-CKF) is used to simulate the convergence of the dynamic model of DR. And, Sage-Husa maximum a posterior (MAP) is employed in filtering progress. The simulation results of the adaptive SR-CKF and CKF are compared, which show that the method proposed in this paper can obtain a fairly accurate heading estimation.
Language英语
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/17188
Collection水下机器人研究室
Corresponding AuthorLiu KZ(刘开周)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
GB/T 7714
Liu KZ,Liu B,Wang YY,et al. Adaptive square-root CKF with application to DR/LBL integrated heading estimation for HOV[C]. Piscataway, NJ, USA:IEEE,2015:1851-1855.
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