Research on a Firepower Allocation Model Based on the Interceptable Factor and HGAPSO Algorithm
程文铮 1张鹏飞 2张润哲 3米江勇 1张宸睿1
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作者信息
1. 中北大学机电工程学院,山西太原 030051;中北大学智能武器研究院,山西太原 030051
2. 中北大学智能武器研究院,山西太原 030051
3. 山西工程科技职业大学汽车工程学院,山西晋中 030606
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摘要
火力分配作为集群目标来袭防御任务规划的关键环节,对提高防御效果具有重要意义.针对高炮反无人机的火力分配问题,将高炮性能指标约束和转火时间约束转化为可拦截因子,提出一种基于可拦截因子的高炮反无人机火力分配模型,减小非线性约束转化为惩罚函数带来的计算量及误差,进而提升整体效能.基于此模型,针对来袭目标与火力节点之间的火力优化匹配问题,采用改进混合遗传粒子群算法(Hybrid GA and PSO,HGAPSO)优化算法对模型进行最优值求解.仿真试验结果表明该模型合理有效,HGAPSO算法有较高的收敛精度和较快的收敛速率.
Abstract
As a key link in the task planning of counter-swarm defense,firepower allocation plays an important role in improving defense effect.Aiming at the problem of firepower allocation in the case of antiaircraft guns against UAVs,a fire allocation model based on the interceptable factor such as the constraints of anti-aircraft gun performance indices and fire shift time is proposed,which reduces the computation and error caused by the nonlinear constraints of the penalty function,and improves overall efficiency.Based on this model,the HGAPSO optimization algorithm is used to find the optimal value of the model for the firepower optimization matching problem of the incoming target and the firepower node.The simulation results show that the model is scientific,reasonable and effective,and the HGAPSO algorithm has higher convergence accuracy and faster convergence rate.
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