The article presents an approach to the control of a UAV on the basis of 3D landmark observations. The novelty of the work is the usage of the 3D RANSAC algorithm developed on the basis of the landmarks’ position prediction with the aid of a modified Kalman-type filter. Modification of the filter based on the pseudo-measurements approach permits obtaining unbiased UAV position estimation with quadratic error characteristics. Modeling of UAV flight on the basis of the suggested algorithm shows good performance, even under significant external perturbations.
This work considers the tracking of the UAV (unmanned aviation vehicle) path on the basis of bearingonly observations including azimuth and elevation angles. The significance of this research becomes clear in the case when GPS either does not work at all or produce the high level of the measurement errors. It is assumed that either UAV's opto-electronic cameras or radar systems are able to capture the angular position of objects with known coordinates and to measure the azimuth and elevation angles of the sight line. Such measurements involve the real position of UAV in implicit form, and therefore some of nonlinear filters such as Extended Kalman filter (EKF) or others may be used in order to implement these measurements for UAV control. However, all such approximate nonlinear filters produce the estimations with unknown bias and quadratic errors. This peculiarity prevents the data fusion in more or less regular way. Meanwhile, there is well-known method of pseudomeasurements which reduces the estimation problem to the linear settings. In this article we develop the modified pseudomeasurement method without bias and with the possibility to evaluate the second moments of the UAV position errors which helps to realize the data fusion. On the basis of this filtering algorithm we develop the control algorithm for tracking of given reference path under external perturbation and noised angular measurements. Modelling examples show the nice performance of the control algorithm.
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