无人机协同多目标攻击空战决策研究

针对超视距空战中多架无人机对空中的多个敌对目标进行协同攻击的决策问题进行了研究。首先,对空战威胁态势进行了分析,基于对各攻击目标至少分配一枚导弹的原则,将协同多目标攻击决策问题转化为导弹目标攻击配对的优化问题并建立其攻击效能评估模型。然后,提出了一种模拟退火遗传算法用于该决策问题的寻优。最后,通过所得最佳导弹目标分配个体求得最终协同攻击决策方案。仿真结果表明所提出的算法能有效地求解协同多目标攻击决策问题,其对最优解的搜索效率明显优于单一的遗传算法。

Air-Combat Decision-Making for UAVs Cooperatively Attacking Multiple Targets

Considering a Beyond Visual Range (BVR) air combat scenario with a group of UAVs versus multiple hostile airborne targets, the decision-making problem for Cooperative Attack on Multiple Targets (CAMT) was investigated. First, the air combat threat situation was analyzed. Based on the principle of each target to be attacked at least being assigned one missile, the decision-making for CAMT was converted into a Missile-Target Assignment (MTA) optimization problem with the establishment of the attack effectiveness evaluation model. Then, a Simulated Annealing Genetic Algorithm (SAGA) was proposed to find out the optimal solution to the MTA problem. Finally, the final decision-making solution to the CAMT was derived from the obtained best missile-target assignment individual. Simulation results show that the proposed method is more effective than Genetic Algorithm (GA) to deal with the decision-making problem for CAMT.