任务驱动的自组织蜂群柔性阵列波束赋形算法研究

根据无人机蜂群构型自组织调整位置和权向量能够实现波束指向特定方向的任务需求，该文提出了一种新颖的任务驱动的自组织蜂群柔性阵列波束赋形算法。首先，建立以无人机蜂群距离为约束、以无人机机载天线坐标位置及权向量为优化变量的波束赋形数学模型。接着，应用Lawson准则简化目标函数，将天线坐标位置及权向量的两类变量优化问题简化为天线坐标位置的单类变量优化，解决了波束赋形模型优化变量耦合带来的求解难题。同时，引入辅助变量，进行约束和复杂目标函数的分离，并通过交替方向乘子法进行求解，降低了包含约束的高度非线性优化问题的求解难度。此外，该文将上述算法扩展至目标方向不精确的应用场景。仿真结果表明，该方法可有效降低波束赋形峰值旁边电平。 

Task-driven Flexible Array Beampattern Synthesis for Self-organized Drone Swarm

This paper proposes a novel task-driven flexible array beampattern synthesis model for self-organized drone swarms according to their task requirements so that they can adjust their positions appropriately and point in a specific direction. First, we formulate the novel beampattern synthesis model using the drone-swarm antenna position and weight vector as the optimization variables and the maximum driving distance as the constraint. Then, the Lawson criterion is used to simplify the objective function, and the two kinds of optimization variables of antenna position and weight vector are reduced to a single kind of variable optimization problem of antenna position, alleviating the optimization difficulty caused by the usage of coupled variables. Simultaneously, auxiliary variables are introduced to separate the constraints from the complex objective function, and the Alternating Direction Method of Multipliers (ADMM）is used to slove the problem, which reduces the difficulty of solving a highly nonlinear optimization problem with constraints,. In addition, we extend this method to a scenario in which the provided Direction Of Arrival (DOA）of interest is imprecise. Simulation results show that the proposed method can obtain lower sidelobe levels than previous methods.