针对目标多元、动态变化的博弈对抗场景下,多雷达协同探测资源与目标/ 环境动态最优匹配的问题,提出了一种基于任务驱动的多雷达协同探测资源动态优化管控方法。首先,分析多雷达协同探测系统资源配置,建立细粒度的探测资源模型;然后,根据实时感知的战场态势和不同类型探测任务,建立以满足任务性能需求为目标的多粒度系统资源参数动态联合优化的数学模型,采用智能优化算法进行快速求解,动态生成资源优化管控方案。最后,通过仿真试验对文中提出方法的有效性进行验证,结果表明,在相同的资源约束下,动态任务驱动的多雷达协同探测资源优化管控方法可以提升多雷达协同探测的效能。

Aiming at the problem of dynamics optimal matching between multi-radar cooperative detection resources and target/ environment in game confrontation scenarios with multiple targets and dynamic changes, a task-driven dynamic optimization management and control method for multi-radar cooperative detection resources is proposed. Firstly, by analyzing the resource allocation of the multi-radar cooperative detection system, a fine-grained detection resource model is given. Then, according to the real-time awareness of the battlefield situation and different types of detection tasks, a mathematical model for the dynamic joint optimization of multi-granularity system resource parameters is established to meet the task performance requirements. Finally, to generate a dynamic resource optimization management and control scheme, an intelligent optimization algorithm is used to solve the mathematical model. Simulation experiments indicate that the proposed method can effectively improve the efficiency of multi-radar cooperative detection under the same resource constraints.

TN957.3