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A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles
Xu, Chenglong1,2,3; Xu CH(徐春晖)2; Wu CD(吴成东)1; Qu DK(曲道奎)2,3; Liu J(刘健)2; Wang Y(王轶群)2; Shao G(邵刚)2
Department水下机器人研究室
Corresponding AuthorXu, Chenglong(xurobot@stumail.neu.edu.cn)
Source PublicationOcean Engineering
ISSN0029-8018
2019
Volume187Pages:1-12
Indexed BySCI ; EI
EI Accession number20192907185103
WOS IDWOS:000487564700007
Contribution Rank1
Funding OrganizationNational Key R&D Program of China (No. 2017YFC03 06800)
KeywordAutonomous underwater vehicle Ultra short baseline Condition-adaptive Confidence measure operator Integrated navigation system
AbstractThis paper presents a novel approach to the design of globally asymptotically stable position and assessment of an USBL-aided integrated navigation based on Condition-adaptive gain Extended Kalman Filter (CAEKF) for the deep water Autonomous Underwater Vehicles (AUVs) subject to uncertainties (e.g., loss of USBL signal, irregular and gross positioning error). Due to the influence of underwater observation conditions, positioning gross error will often appear when exploiting USBL to assist AUV navigation in ocean exploration. Aiming at this kind of problem a method of adding the conditional constraints and confidence assessment to EKF was put forward to filter the positioning value of USBL, and which can make the filtering result more robust and smooth. In addition, in order to reduce positioning error for the deep water vehicle online, an integrated navigation system is constructed by adding the acoustic navigation. Finally, the long voyage of the sea-trials data acquired in suitable sea trials performed in the South China Sea verifying the robustness and practicability of the proposed methodology, a very effective trade-off between accuracy and computational load has been achieved, and which demonstrated that the proposed algorithm outperforms standard navigation algorithms and other classical filtering approaches.
Language英语
WOS SubjectEngineering, Marine ; Engineering, Civil ; Engineering, Ocean ; Oceanography
WOS KeywordTRAJECTORY TRACKING ; AUV NAVIGATION ; ROBUST
WOS Research AreaEngineering ; Oceanography
Funding ProjectNational Key R&D Program of China[2017YFC03 06800]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/25316
Collection水下机器人研究室
Affiliation1.Robot Science and Engineering, Northeastern University, Shenyang, China
2.State Key Laboratory of Robotics, Shenyang Institute of Automation, CAS, Shenyang, China
3.Shenyang SIASUN Robot & Automation Co., LTD, Shenyang, China
Recommended Citation
GB/T 7714
Xu, Chenglong,Xu CH,Wu CD,et al. A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles[J]. Ocean Engineering,2019,187:1-12.
APA Xu, Chenglong.,Xu CH.,Wu CD.,Qu DK.,Liu J.,...&Shao G.(2019).A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles.Ocean Engineering,187,1-12.
MLA Xu, Chenglong,et al."A novel self-adapting filter based navigation algorithm for autonomous underwater vehicles".Ocean Engineering 187(2019):1-12.
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