%0 Journal Article
%T 基于IFS和IPSO算法的干扰资源分配方法<br>Distribution method of jamming resource based on IFS and IPSO algorithm
%A 吴华
%A 史忠亚
%A 沈文迪
%A 陈游
%A 程嗣怡
%J 北京航空航天大学学报
%D 2017
%R 10.13700/j.bh.1001-5965.2016.0870
%X 摘要 针对多干扰系统同时干扰多部雷达的干扰资源分配问题，提出一种基于直觉模糊集（IFS）和改进粒子群优化（IPSO）算法相结合的干扰资源分配方法。利用己方无源探测系统获得的敌方雷达参数，根据IFS理论得到敌方雷达的威胁系数；整合数据库中战场的己方干扰系统与敌方雷达系统信息，从空域、频域、极化方式和干扰样式4个方面定义了匹配度，表示己方干扰系统对敌方雷达系统的干扰效率，得到匹配度矩阵，结合敌方雷达威胁系数建立干扰目标函数；提出一种自适应调整权重、异步变化学习因子、针对离散问题的IPSO算法，并引入补偿粒子进行盲区搜索，求解出最佳干扰决策。仿真表明，本文提出的干扰资源分配方法相较于传统算法最优解正确率更高，且实时性更好。<br>Abstract：In order to solve the problem of distribution method of jamming resource when several jamming systems jam several radar systems, a distribution method of jamming resource based on intuitionistic fuzzy sets (IFS) and improved particle swarm optimization (IPSO) algorithm was proposed. With the parameters of hostile radar detected by passive detecting systems, IFS theory was used to get the threat coefficient of hostile radars. Integrating the data of jamming systems and hostile radars in the database of the battlefield, the paper defines the matched-degree between radar and jamming system to describe jamming efficiency from four aspects:airspace, frequency domain, polarization mode and jamming mode. Combining matched-degree matrix and hostile radar threat coefficient, the jamming target function was obtained. An ISPO algorithm, which adjusts weight self-adaptively, changes learning factors asynchronously and introduces compensating particle to search the blind area, was proposed to get the best jamming distribution method. The simulation shows that the proposed method has better performance in accuracy of best solution and real-time.
%K 资源分配
%K 粒子群
%K 直觉模糊集(IFS)
%K 雷达干扰
%K 多机对多雷达&lt
%K br&gt
%K resource distribution
%K particle swarm
%K intuitionistic fuzzy sets (IFS)
%K radar jamming
%K multi-plane to multi-radar
%U http://bhxb.buaa.edu.cn/CN/abstract/abstract14267.shtml