UAV attitude measurement in the presence of wind disturbance
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ORIGINAL PAPER
UAV attitude measurement in the presence of wind disturbance Jia Zheng1,2 · Hongyan Wang1,2,3,4 · Bingnan Pei1,2 Received: 31 March 2019 / Revised: 19 February 2020 / Accepted: 13 April 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Concentrating on the issue that the existence of wind has an effect on the attitude estimation of unmanned aerial vehicle (UAV) and thereafter degrades the controllability of the UAV, based on the extended Kalman filter (EKF), an approach of UAV attitude estimation is proposed in the presence of wind interference. Firstly, attitude quaternion and drift bias of gyroscope are selected to construct the state vector, and the state equation is established based on the kinematics model of gyroscope. After that, observation equation can be obtained via using the measurement of accelerometer, magnetometer, and airspeed tube. In what follows, the EKF update equation is exploited to determine the UAV attitude. As compared to the traditional EKF and unscented Kalman filter, experimental results show that the proposed algorithm can depress the divergence of attitude angle obviously, upgrade the attitude measurement accuracy considerably, and lower the attitude angle error significantly. Keywords Wind interference · Attitude measurement · Quaternion · Extended Kalman filter
1 Introduction With the rapid development of the microelectromechanical system (MEMS) technology and advanced control technologies, four-rotor aircraft has become a research hot spot in recent years [1, 2]. Compared with other unmanned aerial vehicles (UAVs), four-rotor aircraft has the advantages of small size, low cost, and good maneuverability. Therefore, it has been widely used in transmission line inspections and aerial photography [3, 4]. For four-rotor aircraft, pose estimation is the basis for autonomous flight and therefore has attracted more and more attentions from researchers and engineers [5–9]. To estimate the attitude of UAV, complementary filtering was firstly applied [5]. However, the calculation accuracy obtained via complementary filtering is rather low. To tackle this issue, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) were employed to posture
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Hongyan Wang [email protected]
1
Liaoning Engineering Laboratory of BeiDou High-precision Location Service, Dalian University, Dalian 116622, China
2
Dalian Key Laboratory of Environmental Perception and Intelligent Control, Dalian University, Dalian 116622, China
3
School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
4
Faculty of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China
calculations such that the higher attitude estimation accuracy can be achieved [6, 7]. It is well known that UAV must actually fly within the wind; however, wind interference was not taken into account among the above-mentioned methods. Concerning this problem, a wind parameter estimation algorithm was proposed by exploiting a single-antenna global position
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