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Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation
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
会议录名称Proceedings of the 2015 27th Chinese Control and Decision Conference, CCDC 2015
出版者IEEE
出版地Piscataway, NJ, USA
2015
页码4880-4885
收录类别EI ; CPCI(ISTP)
EI收录号20154401482821
WOS记录号WOS:000375232906054
产权排序2
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
引用统计
文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/17189
专题水下机器人研究室
通讯作者Wang XL(王秀莲)
作者单位1.School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
推荐引用方式
GB/T 7714
Wang XL,Liu KZ,Lin YP,et al. Iterated square root unscented Kalman filter and its application in deep sea vehicle navigation[C]. Piscataway, NJ, USA:IEEE,2015:4880-4885.
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