Optimization of Unmanned Air Vehicle Tactical Formation in War Games

IEEE Access

Felipe Leonardo Lobo Medeiros¹  Angelo Passaro¹  Mark Voskuijl²  Geraldo Mulato De Lima Filho²  Andre Rossi Kuroswiski³  Herman Monsuur³ 

[1] dos Campos-SP, Brazil;Postgraduate Program in Space Science and Technologies, Aeronautics Institute of Technology, S&x00E3;o Jos&x00E9;

关键词: Optimization methods;    computer simulation;    unmanned aerial vehicles (UAV);    autonomous agents;    decision support systems;    computational intelligence;   

DOI  :  10.1109/ACCESS.2022.3152768

来源: DOAJ

【 摘 要 】

War game simulations are decision-making tools that may provide quantitative data about the scenario analyzed by stakeholders. They are widely used to develop tactics and doctrines in the military context. Recently, unmanned air vehicles (UAVs) have become a relevant element in these simulations because of their prominent role in contemporary conflicts, surveillance missions, and search and rescue missions. For instance, it is possible to admit aircraft losses from a tactical formation in favor of the victory of a squadron in a given combat scenario. The optimization of the position of UAVs in beyond visual range (BVR) combat has attracted attention in the literature, considering that the distribution of UAVs can be a determining factor in this scenario. This work aims to optimize UAV tactical formations considering enemy uncertainties such as firing distance and position using six metaheuristics and a high-fidelity simulator. A tactical formation often employed by air forces called line abreast was chosen for the RED swarm for a case study. The objective of the optimization is to obtain a tactical formation of the BLUE swarm that wins the BVR combat against the RED swarm. A procedure to confirm the robustness of the optimization is employed, varying the position of each UAV of the RED swarm up to 8 km from its initial configuration and using the war game approach. A tactical analysis is performed to confirm whether the formations found in the optimization are applicable.

【 授权许可】

Unknown