无人机集群协同侦察覆盖分布式自主优化

UAV cluster cooperative reconnaissance coverage based on distributed autonomous optimization

将无人机集群应用于对多个任务目标同时进行覆盖性协同侦察,是未来无人化作战的重要应用方向之一。研究了多架无人机集群协同侦察时的覆盖范围和能量效率问题,提出一种面向多任务目标的协同侦察覆盖模型。利用博弈论对该模型存在最优的无人机位置和功率进行了证明,提出一种基于分布式自主迭代学习的高能效集群协同侦察覆盖算法,对集群成员覆盖部署的位置和功率进行优化。仿真结果表明,该方法可以实现无人机集群对多个目标进行覆盖侦察部署时的策略稳定收敛,相较于传统方法有效提升了侦察性能,并有效提升集群对区域内多目标的综合侦察能量效率。

Apply UAV cluster to cover multiple mission targets simultaneously, which is one of the important directions of unmanned combat. This paper focuses on studying the coverage area and energy efficiency of the UAV cluster, and put forward a cluster reconnaissance coverage model for multi-task. This model is proved that the best position and power of the UAV cluster exists. This paper present a new algorithm to optimize the position and power of the UAV cluster. The simulation results show that this algorithm can realize the stable convergence. Compared with the traditional method, it can effectively improve the reconnaissance performance and improve the comprehensive reconnaissance energy efficiency of cluster to multiple targets in the region.