UAV swarm confrontation intelligent decision-making technology review

With the advent of unmanned aerial vehicle (UAV) swarm technology, countering UAV swarms has emerged as a pressing challenge requiring immediate attention. Employing UAV swarms with high efficiency-to-cost ratios to counter, disrupt, and intercept enemy UAV swarms has been proven to be a relatively effective countermeasure, prompting extensive research in this field. To comprehensively analyze the progress of intelligent decision-making technology in UAV swarm confrontation, this study initially examined the primary technical challenges faced by intelligent decision-making technology, outlining the establishment and resolution of submodels as the central theme. The study presents three primary models, namely, mathematical programming, game theory, and Markov decision processes, and provides an overview of their current applications and challenges based on relevant theories. Subsequently, the study elaborates on the solution methods for each mathematical model and emphasizes the reinforcement learning-based solving algorithm, highlighting its advantages in the domain of adversarial intelligent decision making. Finally, we summarize the current state and limitations of UAV swarm intelligent decision-making research and offer a perspective on future trends in this field, thereby offering novel avenues for further research.