Accurate two-step filtering for AUV navigation in large deep-sea environment | |
Xu, Chenglong2,4,5; Xu CH(徐春晖)1,3,6; Wu CD(吴成东)5; Liu J(刘健)1,3,6; Qu DK(曲道奎)2,6; Xu F(徐方)2,6 | |
刊名 | Applied Ocean Research |
2021 | |
卷号 | 115页码:1-13 |
关键词 | Underwater navigation Autonomous underwater vehicle (AUV) Ultra-short baseline (USBL) Two-step filtering Backward dead reckoning (Backward-DR) |
ISSN号 | 0141-1187 |
产权排序 | 2 |
英文摘要 | Underwater navigation is a challenging topic in the field of underwater exploration. Accurate and effective underwater navigation is necessary in applications such as 3D seafloor topography scanning, cable laying, deep-sea resource survey and wreck salvage. However, underwater navigation becomes difficult due to the variability and complexity of underwater environment. In the deep-sea environment, the ultra-short baseline positioning system (USBL) is very unstable, and the flying abnormal points and signal loss often occur. To solve these problems, this paper proposes a two-step filtering scheme combined with doppler velocity log (DVL). The first step is to construct a rough Monte Carlo particle filter (MPF) model for the autonomous underwater vehicle (AUV) position, and then select candidate particles with the strategy of minimizing the local backward dead reckoning (DR) errors. In the second step, the results of the first step are further smoothed and optimized using the weighted extended Kalman filter (WEKF). Finally, the robustness and practicability of the proposed method are verified by the multiple data obtained from several sea trials in the South-West Indian Ocean. The results also show that the integrated navigation framework is superior to single navigation method and other traditional filtering methods. The best balance between accuracy and computational cost is achieved. |
资助项目 | National Key R&D Program of China[2017YFC0306800] |
WOS关键词 | KALMAN FILTER ; ALGORITHM |
WOS研究方向 | Engineering ; Oceanography |
语种 | 英语 |
WOS记录号 | WOS:000696962500002 |
资助机构 | Key R&D Program of China under Grant 2018YFC0309901 and the National Key R&D Program of China under Grant 2017YFC0306800 |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/29416] |
专题 | 沈阳自动化研究所_水下机器人研究室 |
通讯作者 | Xu, Chenglong |
作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China 2.Shenyang SIASUN Robot & Automation Co., LTD., Shenyang 110169, China 3.Key Laboratory of Marine Robotics, Liaoning Province, Shenyang 110169, China 4.College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China 5.Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110169, China 6.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China |
推荐引用方式 GB/T 7714 | Xu, Chenglong,Xu CH,Wu CD,et al. Accurate two-step filtering for AUV navigation in large deep-sea environment[J]. Applied Ocean Research,2021,115:1-13. |
APA | Xu, Chenglong,Xu CH,Wu CD,Liu J,Qu DK,&Xu F.(2021).Accurate two-step filtering for AUV navigation in large deep-sea environment.Applied Ocean Research,115,1-13. |
MLA | Xu, Chenglong,et al."Accurate two-step filtering for AUV navigation in large deep-sea environment".Applied Ocean Research 115(2021):1-13. |
个性服务 |
查看访问统计 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论