METHODOLOGY FOR UNPILOTED AIRCRAFT DYNAMICS CONTROL USING CORPORATE GAME THEORY AND MULTI-CHANNEL AERIAL IMAGERY

Abstract

Context. The increase in the number of wars in the modern world stimulates the progress of technological innovation, including the operation of drone systems and modern control systems. The research into mathematical simulations of pursuit, combined with methods of corporate differential games with dynamic constraints, can help throughout the development of the latest vehicle control systems in the civilian and military fields.
Objective. The study is intended to develop a methodology for using multi-channel satellite imagery data in combination with the calculation of unpiloted aircraft motion and dynamic deformations of its elastic elements in vehicle guidance systems derived from pursuit models in cooperative differential game theory.
Method. The methodological framework of the study consists in the integration of data from multi-channel aerospace images into the calculation of motion parameters and, based on this, the deformation of aircraft through the application of game theory methods and the development of advanced information systems to neutralize the opponent’s counteraction. The employment of aerospace photography has emerged as an essential element of many modern technologies in remote vehicle control systems. Despite the lack of reliable vehicle control systems using aerospace photography, they are frequently used and are instrumental in saving countless lives. Meanwhile, aerospace photography is exposed to a large number of excitatory factors that make information on them is commonly misleading, preventing its direct and correct application. The main strategy for increasing the reliability of processing and analytical results of aerospace images in this technique relies on multichannel images, i.e., multiple images of a single object acquired at different radiation frequencies, from distinct positions, angles, or time of shooting. This technique implements natureinspired strategies for optimal management, validated by wildlife evolution. The technology for processing multi-channel images and Integrating these results into vehicle management models remains underdeveloped and needs further refinement.
Results. The study’s scientific novelty consists in establishing methodological foundations for utilizing data from multichannel aerospace imagery, computing unmanned aircraft dynamics within vehicle control systems, and developing advanced information technologies for optimal aircraft management. This approach is based on pursuit models in cooperative differential games and leverages neural network machine learning techniques.
Conclusions. The experiments validate the effectiveness of the proposed approach for enhancing the accuracy of processing and analyzing aerospace images. A methodology for developing vehicle control systems based on pursuit models has been established. Future research will concentrate on adapting the model to new datasets.