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题名: Adaptive square-root CKF with application to DR/LBL integrated heading estimation for HOV
作者: 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)
作者部门: 水下机器人研究室
会议名称: 27th Chinese Control and Decision Conference, CCDC 2015
会议日期: May 23-25, 2015
会议地点: Qingdao, China
会议录: Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
会议录出版者: IEEE
会议录出版地: Piscataway, NJ, USA
出版日期: 2015
页码: 1851-1855
收录类别: EI ; CPCI(ISTP)
ISBN号: 978-1-4799-7016-2
关键词: DR/LBL ; Heading Estimation ; maximum a posterior ; Human Occupied Vehicle ; Adaptive Square-root ; Cubature Kalman filter
摘要: Dead 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.
语种: 英语
产权排序: 1
WOS记录号: WOS:000375232903035
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/17188
Appears in Collections:水下机器人研究室_会议论文

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Recommended Citation:
Liu KZ,Liu B,Wang YY,et al. Adaptive square-root CKF with application to DR/LBL integrated heading estimation for HOV[C]. 见:27th Chinese Control and Decision Conference, CCDC 2015. Qingdao, China. May 23-25, 2015.
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