Loading...
Please wait, while we are loading the content...
Similar Documents
Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter
| Content Provider | Semantic Scholar |
|---|---|
| Author | Luoa, Delin Xiana, Ning Duana, Haibin |
| Copyright Year | 2014 |
| Abstract | 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 eywords: nmanned aerial vehicle (UAV) erial refueling achine vision ose estimation 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. xtend Kalman filter . Introduction Unmanned aerial vehicles (UAVs) play an increasingly imporant role in military actions in the information age. However, the esearch on UAV’s aerial refueling is in an initial state, which in turn auses a bottleneck of its performance [1]. Aerial refueling enables AVs to travel anywhere if needed by extending their durance and ange, which will greatly increase the UAV’s fighting radius and airorne period. UAV’s aerial refueling is an essential part to update its erformance [2]. By measuring the image sequence, it can obtain ne flight’s navigation parameters, such as speed, altitude, and ying direction, which are essential for providing navigation inforation [3–7]. Aircraft visual navigation is a new rapidly developed internaional navigation technology in the past two decades [8]. By using visible light or an infrared camera installed on one vehicle, it is ossible to identify a target such as radar on the ground or another ight vehicle [9,10]. The estimation of the 3D orientation and the osition of an object from its images is called ‘pose estimation’ n the computer vision research community [11]. The modeling f an aircraft has been continuously investigated for many years, nd several nonlinear aircraft models of UAV and tanker have been eveloped using the conventional modeling procedures [12,13]. In 1960, R.E. Kalman described a recursive solution to the iscrete-data linear filtering problem. The Kalman filter is a set of athematical equations that provides an efficient computational ∗ Corresponding author. Tel.: +86 10 8231 7318. E-mail address: hbduan@buaa.edu.cn (H. Duan). 030-4026/$ – see front matter © 2014 Elsevier GmbH. All rights reserved. ttp://dx.doi.org/10.1016/j.ijleo.2014.01.014 © 2014 Elsevier GmbH. All rights reserved. (recursive) means to estimate the state of a process, in a way that minimizes the mean of the squared error [14]. Since the measurement and estimation process is not linear in this situation, an extended Kalman filter is introduced to solve this problem. This work tried to solve the aerial refueling pose estimation problems by using extended Kalman filter, and experimental results are also given for verifying the feasibility of this scheme. 2. The modeling of aircraft boom and turbulence 2.1. Model of the aircraft The nonlinear models of the UAV and the tanker have been developed via flight gear software. Through this software, we choose the “KC-135” model for the tanker, while the F-16 model is a similar nonlinear model for the modeling of the UAV. Then a conventional state vector is can be demonstrated like: F = [v, x, y, , , φ] (1) where V denotes the aircraft velocity, x, y, z represent the position in the earth reference frame, , , φ are the components of the angular velocity in the body reference frame, which denotes pitch, yaw and, roll angle, respectively. 2.2. Model of the aerial refueling To describe the aerial refueling problem, it is necessary to define several reference frames (RFs), that is: Y. Xu et al. / Optik 125 (2 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://hbduan.buaa.edu.cn/papers/2014_YXu_DLuo_NXian_HBDuan.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Aerial photography Algorithm AngularJS Computer vision Email Enea OSE Entity Name Part Qualifier - adopted Extended Kalman filter Frame (physical object) Jumbo frame Lightheadedness Machine vision Mathematics Navigation Nonlinear system Norepinephrine Paper Recursion Reference frame (video) Simulation Television antenna Turbulence Unmanned aerial vehicle Velocity (software development) Verification and validation Verifying specimen Yaws |
| Content Type | Text |
| Resource Type | Article |
