Shaped beam synthesis via semidefinite relaxation (SDR) based on magnitude least squares (MLS)

Abstract

Abstract Shaped beam synthesis of array antennas has attracted the attention of many researchers in recent years and has been widely used in many scenarios. In the previous methods, finding the feasible solution with beam pattern mask constraints has been applied widely, but the solution may not exist with an improper mask. Besides, if the array size is large, the dimension of solution is high, which causes the solution procedure slow. In this study, from the perspective of the magnitude least squares (MLS) model, combined with the semidefinite relaxation (SDR) technique, a new beam pattern synthesis algorithm is derived. The MLS model can guarantee that the solution always exists and can reduce the dimension of solution via introducing auxiliary variable and non‐uniform angular sampling. Simulation results show that this algorithm is effective, can approximate the main lobe well and obtain lower side lobes in contrast to the reference method.

Keywords

• magnitude least squares
• nonuniform angular sampling
• semidefinite relaxation
• shaped beam synthesis