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题名: Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation
作者: Wang XL(王秀莲); Liu KZ(刘开周); Lin YP(林燕平); Liu B(刘本); 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: 4880-4885
收录类别: EI ; CPCI(ISTP)
ISBN号: 978-1-4799-7016-2
关键词: deep sea navigation system ; Human Occupied Vehicle (HOV) ; Unscented Kalman Filter (UKF) ; square root Unscented Kalman Filter (SRUKF) ; iterated square root Unscented Kalman Filter (ISRUKF)
摘要: It is of vital importance to develop a high accuracy and fast convergence algorithm in deep sea navigation system, since location is essential for the sake of scientific survey and safety in very hazard environment. Unscented Kalman Filter (UKF) is the type of filter, which is designed in order to overrun this problem. However, in case of state estimation of the Human Occupied Vehicle (HOV) via the sensor data obtained from a Long Baseline (LBL) acoustic positioning system, a Doppler Velocity Log (DVL), a depth sensor and a motion sensor, where the nonlinearity degree of dynamic model is high and the operating environment is complex, UKF may give inaccurate results. In this study an iterated square root Unscented Kalman Filter (ISRUKF) is presented. An iterated measurement update procedure is included to increase the approximation accuracy of nonlinear state estimates, and a square root version of UKF is conducive to guarantee numerical stability of the algorithm. Compared with the UKF and square root Unscented Kalman Filter (SRUKF), used in deep-sea vehicle navigation system, the ISRUKF algorithm has potential advantages in convergence speed and location accuracy. Extensive experiment researches have been conducted by using the data obtained from previous sea trial to demonstrate its superiority.
语种: 英语
产权排序: 2
WOS记录号: WOS:000375232906054
Citation statistics:
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/17189
Appears in Collections:水下机器人研究室_会议论文

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Recommended Citation:
Wang XL,Liu KZ,Lin YP,et al. Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation[C]. 见:27th Chinese Control and Decision Conference, CCDC 2015. Qingdao, China. May 23-25, 2015.
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