一种基于人工蜂群算法的干扰方案决策方法

A Jamming Scheme Decision-making Method Based on Artificial Bee Colony Algorithm

针对多个无人干扰平台协同有源干扰多个主动雷达制导威胁目标问题,综合考虑无人机与无人艇异同和干扰双方性能,构建干扰效果评估定量化模型。针对干扰方干扰效益评估,确立干扰压制概率、干扰工作频段、干扰样式数量和干扰有效空间4类评估指标,针对导弹目标威胁度评估,确立目标速度、距离、俯仰角和高度4类评估指标,进一步通过混沌映射策略改进传统的人工蜂群算法,以对干扰方案决策模型进行优化求解。仿真结果表明,所提出的干扰决策方法能够有效进行干扰资源分配,相较传统人工蜂群算法而言,采用改进的人工蜂群算法能够有效提升寻优速率。

Aiming at the problem of multiple unmanned jamming platforms cooperating with active jamming against multiple active radar-guided threat targets,a quantitative model of jamming effect evaluation is built by comprehensively considering the similarities and differences between UAV and USV and the performance of both jamming parties.For the evaluation of jamming effectiveness of the jamming party,four classes of evaluation indicators are established,including jamming suppression probability,jamming working frequency band,quantity of jamming patterns and jamming effective space.For the threat degree evaluation of missile targets,four classes of evaluation indicators are established,including the target speed,distance,pitch angle and altitude.The traditional artificial bee colony algorithm(ABC)is further improved by chaotic mapping strategy to optimize the solution of the jamming scheme decision-making model.The simulation results show that the proposed jamming decision-making method can effectively allocate jamming resources,and the improved artificial bee colony algorithm(IABC)can effectively improve the optimization rate compared with the traditional ABC algorithm.