Research on Intelligent Decision Technology for Multi-UAVs Prevention and Control

In recent years, unmanned aerial vehicle technology has been continuously developed and matured, and the level of intelligence has been continuously improved. "Black Flying" brings instability to social security, while UAVs bring convenience to routine production and daily life. In addition, with the continuous development of LAY cluster technology, the future trend of air combat will gradually turn to cluster cooperation. UAV cluster combat will also be changed from concept to reality, from theory to practice. The outstanding performance of artificial intelligence technology in game tasks clearly shows that strategy based on human experience will be difficult to compete with intelligent algorithms in future confrontations. Considering the future demand for multi-UAVs prevention and control, this paper relies on advanced intelligent technologies such as genetic fuzzy trees, multi-objective particle swarm optimization algorithms, reinforcement learning and deep neural networks, aiming at the complex, dynamic, and strong interference, focusing on researching core issues such as the construction of multi-UAVs prevention and control environment models, the generation of compound interception strategies, and the self-learning of autonomous UAV countermeasures. This paper is devoting to exploring new strategy generation methods and proposing solutions to the problem of multi-UAVs prevention and control.