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

Abstract

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.

Keywords

• flexible array
• beampattern synthesis
• self-organized drone swarm
• nonconvex optimization
• radar signal processing