基于改进遗传算法的干扰资源分配方法研究  

Research on Jamming Resource Allocation Based on the Improved Genetic Algorithm

作　　者：郝万兵 张军[1] 张昕[1] HAO Wanbing;ZHANG Jun;ZHANG Xin(Xi'an Electronic Engineering Research Institute,Xi'an 710100)

机构地区：[1]西安电子工程研究所,西安710100

出　　处：《火控雷达技术》2023年第2期56-60,共5页Fire Control Radar Technology

摘　　要：针对分布式对抗系统对敌方分布式雷达的干扰效率问题,设计一种基于二进制编码的改进遗传算法,用于提高系统自主干扰资源分配效率。首先,通过前期侦察,获取雷达的性能参数,得到每部干扰机对每部雷达的干扰效能参数,生成干扰效能矩阵,以干扰总效能最大化为原则,构建目标效能函数;然后根据干扰机与雷达的数量差异建立干扰资源分配模型,确定约束条件;最后利用改进遗传算法,通过选择、交叉、变异、淘汰、继承等算子操作对模型的最优值进行求解,并给出具体算法流程。Matlab仿真结果表明,改进算法需要更少的迭代次数,能够得到最优解,能够较好地解决文中构设场景下的分布式协同干扰资源优化分配问题。To address the jamming efficiency of distributed countermeasure systems to the enemy's distributed radar,an improved genetic algorithm based on binary encoding was designed to improve the efficiency of autonomous jamming resource allocation.Firstly,through the reconnaissance in early stages,the radar performance parameters as well as the jamming effectiveness parameters of each jammer to each radar were obtained,and a jamming effectiveness matrix was generated.A target effectiveness function was constructed based on the principle of maximizing the total jamming effectiveness.Secondly,a jamming resource allocation model was established according to the number difference between the jammers and the radar,and the constraint conditions were determined.Finally,the improved genetic algorithm was used to solve the optimal value of the model through selection,crossover,mutation,elimination,inheritance,and other operator operations,and the specific algorithm process was given.MATLAB simulation results show that the improved algorithm requires less iteration and can obtain the optimal solution,which can solve the problem of optimal resource allocation in the distributed cooperative jamming scenario constructed in this paper.

关 键 词：分布式对抗 干扰资源分配 遗传算法 干扰效能 

分 类 号：TN95[电子电信—信号与信息处理]