复杂多方向威胁下的导弹预警雷达优化部署方法

Deployment optimization method for missile early warning radar under complex and multi-directional missile threats

针对现有导弹预警雷达部署相对独立、协同困难,难以满足大规模对抗场景的现状,从远程预警雷达、跟踪识别雷达、机动式预警雷达不同的任务特点出发,建立应对复杂多方向威胁的多型导弹预警雷达优化部署模型,在满足最优覆盖、协同交接、目标识别等任务约束下,解决雷达协同部署问题。针对所提模型设计了一种基于云自适应的分区优化离散粒子群(CPBPSO)算法,通过设计分区编码策略缩减算法求解空间、加入云自适应变异算子提高算法全局寻优和局部跳出能力,使算法更适用于导弹预警雷达部署问题的处理。实例验证了所提模型在求解单方向、多方向威胁场景部署问题的可行性,对比分析了CPBPSO算法的有效性,基本满足导弹预警雷达最优化协同部署的需求。

An optimized deployment model of multiple missile early warning radars is established based on the distinct mission characteristics of early warning radar,tracking and identification radar,and transportable early warning radar in order to address the situation where the deployment of existing missile early warning radars is relatively independent,difficult to cooperate with,and difficult to meet the large-scale operation scenario.Under the constraints of optimal coverage,cooperative handover,and target identification,the cooperative deployment of early warning radars is solved.A cloud adaptive partition optimization binary particle swarm optimization(CPBPSO)algorithm is designed.In order to make the algorithm more suited for solving early warning radar deployment problems,the partition coding strategy is used to shrink the algorithm’s solution space.The cloud adaptive mutation operator is then added to enhance the algorithm’s global optimization and local jumping capability.The example verifies the feasibility of the model in solving the deployment problem of single-direction and multi-direction threat scenarios and analyzes the effectiveness of the CPBPSO algorithm,which basically meets the needs of the optimal cooperative deployment of missile early warning radar.