ANALYSIS OF THE TACTICAL AND TECHNICAL CHARACTERISTICS AND TACTICS OF USING EXISTING STRIKE FPV-DRONES

Abstract

The subject of the study in the article is the tactical and technical characteristics and tactics of strike FPV drones. The aim is to analyze the tactical and technical characteristics of existing strike FPV drones and how they are commonly used in combat operations. Tasks: identification and classification of unmanned aerial vehicles, analysis of the main technical parameters of strike FPV drones, research on the methods and strategies for the use of strike FPV drones in combat, investigation of the vulnerabilities of strike FPV drones to electronic warfare means and methods of protection against them, and development of recommendations for improving the technical characteristics and combat capabilities of strike FPV drones. The methods used are comparative analysis, data clustering, analytical and empirical methods of analyzing the use of strike FPV drones in military conflicts, particularly in the Russian-Ukrainian war. The following results were obtained. A structured analysis of the tactical and technical characteristics and tactics of strike FPV drones has been conducted, highlighting their advantages and disadvantages and forming proposals for improvement. The practical significance of the obtained results can serve as a basis for enhancing existing strike FPV drones, the development of new models, and increasing their effectiveness in combat conditions. Conclusions: The analysis of the tactical and technical characteristics and the tactics of use of strike FPV drones has shown that such unmanned aerial vehicles significantly influence the course of modern military operations, providing new opportunities for effective precision strikes on enemy equipment and personnel, conducting reconnaissance, adjusting fire, and transportation functions. Strike FPV drones demonstrate high efficiency due to their maneuverability, relatively low cost, and the ability to be modified for specific combat tasks. At the same time, their vulnerability to electronic warfare necessitates further improvement of communication and control systems. The analysis of experience also allowed the proposal of possible directions for further research to increase the effectiveness of unmanned aerial vehicles during combat operations. Electronic warfare can significantly impact the control and navigation channels of FPV drones, but it does not affect the image acquisition channels in the targeting systems of strike FPV drones. Therefore, to increase the probability of hitting enemy equipment under electronic warfare conditions, the use of self-guiding strike FPV drones is promising. One condition for successfully implementing such a system is the development of real-time image segmentation algorithms received from the drone. Modern computer vision methods, including those with artificial intelligence elements, can be used for this. The result of such image segmentation should be the division of the image into artificial objects (targets) and natural objects (backgrounds). With such a system, the operator of a strike FPV drone only needs to capture the target by selecting the object on the screen, after which the automation will take over further actions. Also, image decryption is one of the most complex and important stages of image processing in the targeting systems of strike FPV drones. The quality of the segmentation operation of the optoelectronic image affects the result of image processing. Therefore, in further research, it is advisable to focus on image segmentation in the targeting systems of strike FPV drones. The research results can form the basis for developing recommendations for optimizing the design, selecting components, and training operators, which will increase the combat effectiveness of strike FPV drones in future wars. The research was conducted with grant support from the National Research Foundation of Ukraine within the framework of the "Science for Strengthening Ukraine's Defense Capability" competition, project title "Information Technology for Automated Image Segmentation of Objects in Strike FPV Drone Targeting Systems Based on Swarm Intelligence Algorithms," registration number 2023.04/0153.