A hybrid deep sea navigation system of LBL/DR integration based on UKF and PSO-SVM
文献类型：期刊论文
| 作者 | 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)
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| 刊名 | 机器人
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| 出版日期 | 2015 |
| 卷号 | 37期号:5页码:614-620 |
| 关键词 | unscented Kalman filter (UKF) particle swarm optimization (PSO) support vector machine (SVM) deep sea navigation system human occupied vehicle (HOV) |
| ISSN号 | 1002-0446 |
| 产权排序 | 1 |
| 中文摘要 | 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. |
| 收录类别 | EI ; CSCD |
| 语种 | 英语 |
| 源URL | [http://ir.sia.cn/handle/173321/17323]  |
| 专题 | 沈阳自动化研究所_水下机器人研究室 |
| 推荐引用方式 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. |
入库方式： OAI收割
来源：沈阳自动化研究所
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