摘要
针对敌方多目标雷达,如何合理有效的分配干扰资源,取得最佳干扰效益的问题,建立干扰资源优化分配模型。考虑到实际作战环境下的约束条件和干扰需求,对于约束条件增多时,传统算法求解速度慢。因此,提出一种将模拟退火算法应用到遗传算法中,以提高遗传算法局部搜索性能,增强遗传算法进化能力的遗传模拟退火算法。在每次进化产生下一代种群个体需经过模拟退火算法改进,并在每次迭代结束之前进行降温操作保证遗传算法和模拟退火算法具有相同的收敛方向和速度。仿真结果表明,与模拟退火算法比较,该方法具有较好的搜索最优解速度和可靠性。上述方法提供的分配方案对提高雷达干扰智能决策系统有一定的作用。
In modern electronic warfare,in view of the multi-target radar of enemy,it plays an important role in reasonable and effective allocation of resources to achieve the best interference efficiency.The interference resource optimization allocation model is established based on suppress the jammer power which combined with the actual operational environment.Taking into account the constraints and needs of actual combat interference environment,the traditional algorithm is slow when the constraints increase.In order to improve the local search performance of the GA and the evolutionary ability of GSA,a SA is put forward to be applied to the GA.The next generation of population individuals produced by each evolution is improved by SA,and cooling operation before the end of each iteration operation ensures that the GA and SA have the same direction and speed of convergence.The simulation results show that,compared with SA,this method has a better search optimal solution speed and reliability.The allocation scheme provided by the method has a certain effect on improving the intelligent decision system for radar jamming.
出处
《计算机仿真》
CSCD
北大核心
2016年第5期23-26,41,共5页
Computer Simulation
基金
国家自然科学基金(61170120)
关键词
干扰
遗传算法
模拟退火算法
雷达
Jamming
Genetic algorithm
Simulated annealing algorithm
Radar
