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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)
Department水下机器人研究室
Source Publication机器人
ISSN1002-0446
2015
Volume37Issue:5Pages:614-620
Indexed ByEI ; CSCD
EI Accession number20154801627396
CSCD IDCSCD:5542561
Contribution Rank1
Funding OrganizationNational High Technology Development Program of China(2009AA093302, 2014AA09A110) ; the Chinese Academy of Strategic Leading Science and Technology Special(XDA11040104)
KeywordUnscented Kalman Filter (Ukf) Particle Swarm Optimization (Pso) Support Vector Machine (Svm) Deep Sea Navigation System Human Occupied Vehicle (Hov)
AbstractIn 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.
Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/17323
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, Chinese Academy of Sciences, Shenyang, China
2.University of Chinese Academy of Sciences, Beijing, China
Recommended Citation
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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