The LCMV Pattern Synthesis Based on Partition Optimization for Cooperative Detection using UAV Swarm

Abstract

Cooperative detection using unmanned aerial vehi-cle (UAV) swarm has many obvious advantages such as better spatial resolution, high flexibility and lower cost so that it is favored for military. Compared with the traditional dense planar array, the pattern of the UAV swarm suffers higher sidelobe level and serious grating lobe effect due to its three-dimensional (3D) random and sparse array. In this paper, we propose a lineary constrained minimum variance (LCMV) method based on partition optimization for pattern synthesis of the 3D UAV swarm array. Firstly, the null-to-null beamwidths of the azimuth and elevation are calculated, respectively. Then the azimuth-elevation pattern is divided into four partitions including three sidelobe regions and one mainlobe region. Finally, the levels of above three sidelobe regions are individually optimised to each reference value via LCMV algorithm. The proposed method can achieve different sidelobe levels and has faster convergence over the state-of-the-art two-dimensional union pattern synthesis method. Simulation results validate the effectiveness of the proposed method.