Multi-Step Decision-Making Target Assignment Method for Multi-UAV Cooperative Air Combat Based on IIP-GA

In the background of air combat, multiple unmanned aerial vehicle (multi-UAV) cooperative target assignment has always been a major challenge. Most previous research has the problem of only modeling single-step static air combat scenarios and the assignment effect cannot be quantified. To solve these problems, this paper first establishes a reasonable and effective single-step target assignment model. Furthermore, two novel rules for updating the assignment scheme are proposed to cope with the continuous changes in the entire battlefield. On this basis, a multi-step decision-making target assignment method based on an improved initial population genetic algorithm (IIP-GA) is proposed. The IIP-GA uses the correlation between two adjacent decision-making schemes to improve the traditional random initial population genetic algorithm (RIP-GA), which can increase the possibility of obtaining the optimal assignment scheme with the highest reward value. Finally, through simulation experiments, the rationality, accuracy, and efficiency of the proposed method are verified by some quantitative data from air combat.