To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).

针对现代复杂雷达网络对飞机的射频威胁，我们研究了无人机编队雷达对抗，旨在解决系统对抗下的雷达干扰资源分配问题。提出了一种基于改进萤火虫算法(FA)的干扰资源分配方法。首先，通过模糊综合评价对影响辐射源威胁程度和干扰效率的综合因素进行量化。此外，确定干扰效率矩阵和分配模型的目标函数，建立干扰资源分配模型。最后，将变异算子和自适应启发式算法集成到FA算法中，搜索干扰资源分配方案。仿真结果表明，改进后的FA算法可以弥补FA算法的不足。改进后的FA算法为飞机任务分配提供了更加科学合理的决策方案，能够有效应对敌方雷达网络的战场威胁。而且，在收敛精度和速度以及算法稳定性方面，改进的FA算法优于模拟退火算法（SA）、生态位遗传算法（NGA）、改进的离散杜鹃算法（IDCS）、突变萤火虫算法。算法（MFA）、布谷鸟搜索和萤火虫算法（CSFA）和最佳邻居萤火虫算法（BNFA）。改进后的FA算法为飞机任务分配提供了更加科学合理的决策方案，能够有效应对敌方雷达网络的战场威胁。而且，在收敛精度和速度以及算法稳定性方面，改进的FA算法优于模拟退火算法（SA）、生态位遗传算法（NGA）、改进的离散杜鹃算法（IDCS）、突变萤火虫算法。算法（MFA）、布谷鸟搜索和萤火虫算法（CSFA）和最佳邻居萤火虫算法（BNFA）。改进后的FA算法为飞机任务分配提供了更加科学合理的决策方案，能够有效应对敌方雷达网络的战场威胁。而且，在收敛精度和速度以及算法稳定性方面，改进的FA算法优于模拟退火算法（SA）、生态位遗传算法（NGA）、改进的离散杜鹃算法（IDCS）、突变萤火虫算法。算法（MFA）、布谷鸟搜索和萤火虫算法（CSFA）和最佳邻居萤火虫算法（BNFA）。