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.

In this paper, an optimal singular stochastic control problem is considered. For this model, a general stochastic maximum principle is obtained by using a time transformation. This is the first version of the stochastic maximum principle that covers nonlinear cases.

The navigation of autonomous underwater vehicles is a major scientific and technological challenge. The principal difficulty is the opacity of the water media for usual types of radiation except for the acoustic waves. Thus, an acoustic transducer (array) composed of an acoustic sonar is the only tool for external measurements of the AUV attitude and position. Another difficulty is the inconstancy of the speed of propagation of acoustic waves, which depends on the temperature, salinity, and pressure. For this reason, only the data fusion of the acoustic measurements with data from other onboard inertial navigation system sensors can provide the necessary estimation quality and robustness. This review presents common approaches to underwater navigation and also one novel method of velocity measurement. The latter is an analog of the well-known Optical Flow method but based on a sequence of sonar array measurements.

This work considers the tracking of the UAV (unmanned aviation vehicle) path on the basis of bearing-only 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.

An automatic landing of an unmanned aerial vehicle (UAV) is a non-trivial task requiring a solution of a variety of technical and computational problems. The most important is the precise determination of altitude, especially at the final stage of approaching to the earth. With current altimeters, the magnitude of measurement errors at the final phase of the descent may be unacceptably high for constructing an algorithm for controlling the landing manoeuvre. Therefore, it is desirable to have an additional sensor, which makes possible to estimate the height above the surface of the runway. It is possible to estimate all linear and angular UAV velocities simultaneously with the help of so-called optical flow (OF), determined by the sequence of images recorded by an onboard camera, however in pixel scale. To transform them into the real metrical values it is necessary to know the current flight altitude and the camera angular position values. The critical feature of the OF is its susceptibility to the camera resolution and the shift rate of the observed scene. During the descent phase of flight, these parameters change at least one hundred times together with the altitude. Therefore, for reliable application of the OF one needs to coordinate the shooting parameters with the current altitude. However, in case of the altimeter fault presence, the altitude is also still to be estimated with the aid of the OF, so one needs to have another tool for the camera control. One of the possible and straightforward ways is the camera resolution change by pixels averaging in computer part which performed in coordination with theoretically estimated and measured OF velocity. The article presents results of such algorithms testing from real video sequences obtained in flights with different approaches to the runway with simultaneous recording of telemetry and video data.