Abstract
Flying vehicle’s navigation, direction, and control in real-time results in the design of a strap-down inertial navigation system (INS). The strategy results in low accuracy, performance with correctness. Aiming at the attitude estimation problem, many data fusion or filtering methods had been applied, which fail in many cases, which attains the nonlinear measurement model, process dynamics, and high navigation range. The main problem in unmanned aerial vehicles (UAVs) and flying vehicles is the determination of attitude angles. A novel attitude estimation algorithm is proposed in this study for the unmanned aerial vehicle (UAV). This research article designs two filtering algorithms for fixed-wing UAVs which are nonlinear for the attitude estimation. The filters are based on Kalman filters. The unscented Kalman filter (UKF) and cubature Kalman filter (CKF) were designed with different parameterizations of attitude, i.e., Euler angle (EA) and INS/unit quaternion (UQ) simultaneously. These filters, EA-UKF and INS-CKF, use the nonlinear process and measurement model. The computational results show that among both filters, the CKF attains a high accuracy, robustness, and estimation for the attitude estimation of the fixed-wing UAV.
Highlights
The strap-down inertial navigation system (SINS) occupies an electromechanical system (EMS) that attains low cost and consumption of power. e performance and robustness of EMS based on the INS improve due to nonlinear filtering, which helps in attaining attitude estimation and tracking of the unmanned aerial vehicles (UAVs) [5]
E information of attitude and position is attained by the INS with abundant update rate, and information combination is the way to increase the accuracy. e INS attains the capability to estimate the pose of the camera using the Kalman filter (KF) where the navigation measurement system is for visual measurement and update [6,7,8]
In reference to [9], the unscented Kalman filter (UKF) is proposed for the flight of the UAV. e state equation model is based on the attitude
Summary
In this phase, the filter generates the prediction of system state vector xεRn at time t + 1. E error covariance matrix is denoted by EεRn×n and is shown in the following equation at time t. Y(t + 1) h(x(t + 1|t), v(t + 1)), where the estimation of the exact system state is denoted by x, F(t) is the process model matrix, u is a control vector, w denotes the process noise, K(t) is a process noise model matrix, and v is the observation noise. E process noise is denoted by Q. e matrix E should verify the following condition. E(t + 1) − cov[x(t + 1|t) − x(t + 1|t)] ≥ 0
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