The Application Of Nonlinear Filtering To Uav Relative Navigation

Cooperative formation flight is a kind of new application method of Unmanned Aerial Vehicle (UAV) which has a wide prospect and huge technical advantage and represents the future developing direction. Therefore, many researchers pay attention to cooperative formation flight. This paper whose background is cooperative formation flight does research on relative navigation between UAVs in formation. By contrast with multiple relative navigation schemes, INS/VisNav is chosen as the project whose core equipment is VisNav. In the same time, various relative navigation filters are designed. The major contents are as follows:On the basis of introducing the attitude description and attitude kinematics of UAV, according to the"leader-follower"formation mode, the relative kinematics model is derived, the measurement models of gyro, accelerometer and VisNav are provided, and the observability of VisNav is analyzed on the condition of single vector, double vectors, three vectors and multiple vectors measurements. Therefore, the working premise of VisNav could be determined. The least square algorithm is applied to process the data of VisNav to estimate the relative attitude and position. The results of simulation show that the accuracy of least square algorithm would be influenced by the relative position of UAVs in formation.The INS/VisNav relative navigation filter is designed which is based on extended Kalman filtering (EKF). The INS measurement dates are used to substitute the angle velocity and acceleration in relative motion equations. As a result, the relative INS equations could be acquired. In the same time, the Jacobian matrix and measurement matrix of EKF could be derived. In the premise of formation flight of leader and follower, the effect of gravity could be ignored. Therefore, the algorithm is simplified and the calculation velocity is promoted.To improve the convergence rate and estimated accuracy, the relative navigation filter is designed which is based on Sigma-Point Kalman filtering. Firstly, the Unscented Kalman filtering and Central Differential Kalman filtering are introduced. By contrast the two algorithms, the common points could be found which are that the Sigma-Point is used to approximate the nonlinear equations. As a result, the Sigma-Point Kalman filtering is summarized and concluded. For the quaternion normalization, an unconstrained three-component vector is used to presented an attitude error quaternion which could confirm the non-singular of error covariance matrix. The simulation results show that the algorithm has evident quick convergence rate.Considering that when the system noise follows non-Gaussian probability distribution, the Kalman filtering couldn't estimate accurately, filter divergence would be possible to happen on extreme condition. The relative navigation filter is designed which is based on Huber-Based filtering that is a combined minimum l1 and l2 norm estimator which could exhibit robustness for the perturbed Gaussian probability distribution. The Huber-Based filtering and Sigma-Point Kalman filtering are combined. As a result, a new algorithm which is called robust Sigma-Point Kalman filtering is presented. The relative navigation filter is designed which is based on robust Sigma-Point Kalman filtering. Finally, the simulation was done.The accuracy and reliability are two important standards which are used to evaluate the relative navigation system. This paper presents the INS/VisNav/GPS relative navigation system. Using the hierarchically decentralized structure, the information filtering is applied to fuse the dates from INS, VisNav and GPS system. Contrast with INS/VisNav, this system could promote the accuracy and reliability.