A.A. Kostoglotov1, A.S. Penkov2, V.P. Tavunov3

1,2 Don State Technical University (Rostov-on-Don, Russia)

3 Rostov-on-Don Scientific Research Institute of Radio Communications (Rostov-on-Don, Russia)

1kostoglotov@icloud.com, 2 pencha@mail.ru, 3 tavunov@mail.ru

An effective assessment of the motion parameters of aircraft based on information at the output of radar and other surveillance sources is the basis for solving a number of important flight safety tasks, which is associated with the need to synthesize tracking algorithms. At the same time, the quality of tracking is determined not only by the accuracy of the measurement data, but also by the maneuverability of the object.

To ensure target tracking by modern radar systems, the Kalman filter is traditionally used, which is based on a kinematic motion model. For many aircraft, this model adequately describes real motion for most of the flight. But when maneuvering a target, associated with a change of echelon, course, takeoff and landing, this model does not correspond to real movement. Its use in trajectory processing under these conditions can lead to large dynamic errors.

Dynamic errors can lead to disruption of tracking a maneuvering target, false maneuver definitions, and systematic errors in estimating the actual route of the aircraft. As is known, for a wide class of objects moving at a constant speed, U-turns, anti-roll maneuvers, atmospheric turbulence can be considered as trajectory perturbations. At the same time, acceleration and maneuver are correlated in time, which makes it possible to describe such effects based on the expansion of the state space and use the method of the forming filter in order to obtain the structure of the motion model with parameters related to the duration and intensity of the maneuver. This allows you to achieve a better match of the model to the real movement. The resulting model requires adaptation of the parameters of the duration and intensity of the maneuver according to a variety of available reference trajectories.

However, when constructing models of three-dimensional motion, the choice of parameters becomes ambiguous, for example, an increase in the power of a random disturbance in the motion model, in turn, will lead to a decrease in the accuracy of estimating the coordinates and parameters of the object's motion in rectilinear uniform motion. Moreover, in real conditions, the presence of uncertainty in the matrices of the intensity of observation noise and condition caused by a priori unknown random influences on the observation channel, as well as unpredictable scenarios of aircraft movement, lead to a decrease in the effectiveness of the tracking system. Such negative factors lead to the need to adapt the parameters of the tracking filter according to the data obtained as a result of the operation of the radar station in a real background-target environment. Identification of model parameters can be implemented based on optimization according to a complex criterion that takes into account the random and systematic error in estimating motion parameters. At the same time, due to the random nature of the initial data, as is known, the effectiveness of gradient methods is significantly inferior to zero-order methods. One of the most effective approaches to optimize the function of many variables under conditions of uncertainty is the method of sequential search for a Nelder-Meade or deformable polyhedron.

The purpose of the work is to increase the accuracy of estimating the parameters of the aircraft movement in maneuver conditions.

The task to be solved is to develop a method for synthesizing algorithms for estimating the motion parameters of aircraft based on a model of a maneuvering object with subsequent optimization of parameters by the deformable polyhedron method.

Kostoglotov A.A., Penkov A.S., Tavunov V.P. Method of structural-parametric synthesis of algorithms for tracking maneuvering aircraft using a non-smooth optimization procedure. Information-measuring and Control Systems. 2024. V. 22. № 1. P. 15−23. DOI: https://doi.org/10.18127/j20700814-202401-03 (in Russian)

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