Abstract

The article proposes a methodology for determining the optimal composition of reconnaissance-strike complexes formed on the basis of unmanned aerial vehicles (UAVs), taking into account the specifics of combat missions, the operational environment, and the technical characteristics of the systems.

The relevance of the study is driven by the rapid increase in the role of UAVs in contemporary armed conflicts, the need to improve the effectiveness of their combat employment, and to minimize losses of equipment and personnel.

The methodology is based on a comprehensive approach that includes: classification of UAVs by functional role, formalization of mission parameters, the use of mathematical models of interaction within a swarm, and optimization algorithms.

The work takes into account results of recent research in the fields of autonomous control, trajectory planning, resource allocation, and operations under adversary countermeasures. Special attention is given to the use of artificial intelligence technologies, adaptive decision-making algorithms implemented at the edge (edge AI), as well as secure interaction between swarm elements via blockchain architectures.

The proposed approach enables formation of balanced reconnaissance-strike complexes tailored to a given combat situation, thereby increasing the effectiveness of reconnaissance, strike, and support tasks. It is aimed at achieving a balance between combat effectiveness, system cost, and resilience to kinetic and electronic-warfare effects. Practical implementation of the methodology demonstrated effectiveness during simulation of a strike against air-defense assets of a notional adversary. The results can be used for operational planning and for the development of automated UAV control systems.

Keywords: reconnaissance-strike complexes; unmanned aerial vehicles (UAVs); mathematical model; optimization; objective function; initial parameters; effectiveness.

FULL TEXT (in Ukrainian)

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The article was submitted 04.11.2025

© Semenenko, O.M., Slyusarenko, M.O., Korkina, N.O., Dobrovolskyi, Yu.B., Yarmolchyk, M.O.
Creative Commons Attribution 4.0 International License (CC BY 4.0)