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A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM
Liu B(刘本); Liu KZ(刘开周); Wang YY(王艳艳); Zhao Y(赵洋); Cui SG(崔胜国); Wang XH(王晓辉)
作者部门水下机器人研究室
关键词Unscented Kalman Filter (Ukf) Particle Swarm Optimization (Pso) Support Vector Machine (Svm) Deep Sea Navigation System Human Occupied Vehicle (Hov)
发表期刊机器人
ISSN1002-0446
2015
卷号37期号:5页码:614-620
收录类别EI ; CSCD
EI收录号20154801627396
CSCD记录号CSCD:5542561
产权排序1
资助机构National High Technology Development Program of China(2009AA093302, 2014AA09A110) ; the Chinese Academy of Strategic Leading Science and Technology Special(XDA11040104)
摘要In order to improve the navigation accuracy of human occupied vehicle (HOV) precisely and efficiently, an innovative hybrid approach based on unscented Kalman filter (UKF) and support vector machine (SVM) is proposed to fuse integrated navigation data. HOV is generally equipped with long baseline (LBL) acoustic positioning system and dead reckoning (DR) as an integrated navigation system. UKF is adopted to estimate the state of the dynamic model because of its good performance in filtering nonlinear problems. An accurate and stable filtering result can be obtained when both LBL and DR are online. At the same time, SVM is utilized to train DR information with the result when LBL outrages, and the particle swarm optimization (PSO) algorithm is employed for SVM parameters optimization. Therefore, the integrated navigation system can maintain a good performance when the LBL is off-line. Simulation results with the real navigation data of Jiaolong HOV show that the methodology proposed here is able to meet the needs of HOV application.
语种英语
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文献类型期刊论文
条目标识符http://ir.sia.cn/handle/173321/17323
专题水下机器人研究室
通讯作者Liu KZ(刘开周)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
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GB/T 7714
Liu B,Liu KZ,Wang YY,et al. A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM[J]. 机器人,2015,37(5):614-620.
APA Liu B,Liu KZ,Wang YY,Zhao Y,Cui SG,&Wang XH.(2015).A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM.机器人,37(5),614-620.
MLA Liu B,et al."A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM".机器人 37.5(2015):614-620.
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