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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(IPSO)算法相结合的资源分配方法。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四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;最后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。
Aiming at the problem of multi-jamming system interfering with the resource allocation of multiple radars simultaneously, a resource allocation method based on intuitionistic fuzzy set (IFS) and improved particle swarm optimization (IPSO) is proposed. According to the theory of IFS, the threat coefficient of the enemy’s radar is obtained by using the enemy’s radar parameters obtained by one’s own passive detection system. The information of one’s own interfering system and the enemy’s radar system in the battlefield is integrated from the space, frequency domain, polarization mode and interference The four aspects of the pattern define the matching degree as the interference efficiency of one’s own interfering system to the enemy radar system and the matrix of matching degree to obtain the interference objective function based on the threat coefficient of the enemy radar. Finally, an adaptive adjustment weight, asynchronous learning factor , IPSO algorithm for discrete problems, and the introduction of compensation particles for blind search, solve the best interference decision. Simulation results show that the proposed resource allocation method has higher accuracy and better real-time performance than the traditional algorithm.