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 resourc...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).展开更多
针对传统最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计存在计算量大、估计精度差等问题,本文提出一种采用改进帝王蝶优化算法(improved monarch butterfly optimization algorithm,IMBO)的ML-DOA估计方法。I...针对传统最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计存在计算量大、估计精度差等问题,本文提出一种采用改进帝王蝶优化算法(improved monarch butterfly optimization algorithm,IMBO)的ML-DOA估计方法。IMBO算法通过精英反向学习策略对初始帝王蝶种群进行优化,得到适应度值较优的初始帝王蝶个体,进而能够改善帝王蝶种群的多样性;引入差分进化算法启发的变异操作以及自适应策略对帝王蝶个体的寻优方式进行改进,扩大了算法的搜索空间;引入了高斯-柯西变异算子,自适应调整变异步长,避免算法陷入局部最优。将IMBO应用于ML-DOA,实验表明,与传统的DOA估计算法相比,在不同信源数目、信噪比以及种群数量下,本文提出的算法收敛性能更好,均方根误差更低,运算量更小。展开更多
文摘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).
文摘针对传统最大似然波达方向(maximum likelihood direction of arrival,ML-DOA)估计存在计算量大、估计精度差等问题,本文提出一种采用改进帝王蝶优化算法(improved monarch butterfly optimization algorithm,IMBO)的ML-DOA估计方法。IMBO算法通过精英反向学习策略对初始帝王蝶种群进行优化,得到适应度值较优的初始帝王蝶个体,进而能够改善帝王蝶种群的多样性;引入差分进化算法启发的变异操作以及自适应策略对帝王蝶个体的寻优方式进行改进,扩大了算法的搜索空间;引入了高斯-柯西变异算子,自适应调整变异步长,避免算法陷入局部最优。将IMBO应用于ML-DOA,实验表明,与传统的DOA估计算法相比,在不同信源数目、信噪比以及种群数量下,本文提出的算法收敛性能更好,均方根误差更低,运算量更小。
