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Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter
Xu, Yan ; Luo, Delin ; Xian, Ning ; Duan, Haibin ; Luo DL(罗德林)
刊名http://dx.doi.org/10.1016/j.ijleo.2014.01.014
2014
关键词FORMATION FLIGHT VEHICLES
英文摘要National Key Basic Research Program of China (973 Project) [2013CB035503]; National Magnetic Confinement Fusion Research Program of Chinaunder [2012GB102006]; Aeronautical Foundation of China [20135851042, 20115868009]; Open Funding Project of State Key Laboratory of Virtual Reality Technology of and Systems [BUAA-VR-13KF-01]; In recent years, many pose estimation algorithms were developed, and have been successfully applied to solve unmanned aerial vehicle (UAV) aerial refueling pose estimation problems. This paper mainly focuses on solving this problem under serious turbulences circumstance. The extended Kalman filter is a set of mathematical equations to estimate the state of a process, which is able to support estimations of past, present, and even future states. In reference to previous papers and some simulations, we build up the noise models of refueling boom and atmospheric turbulence. Then, an extend Kalman filter is adopted to solve the pose estimation problem in UAV aerial refueling with serious turbulences. The experimental results demonstrate the feasibility and effectiveness of our proposed approach. (C) 2014 Elsevier GmbH. All rights reserved.
语种英语
出版者ELSEVIER GMBH, URBAN & FISCHER VERLAG
内容类型期刊论文
源URL[http://dspace.xmu.edu.cn/handle/2288/92748]  
专题信息技术-已发表论文
推荐引用方式
GB/T 7714
Xu, Yan,Luo, Delin,Xian, Ning,et al. Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter[J]. http://dx.doi.org/10.1016/j.ijleo.2014.01.014,2014.
APA Xu, Yan,Luo, Delin,Xian, Ning,Duan, Haibin,&罗德林.(2014).Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter.http://dx.doi.org/10.1016/j.ijleo.2014.01.014.
MLA Xu, Yan,et al."Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter".http://dx.doi.org/10.1016/j.ijleo.2014.01.014 (2014).
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