Open AccessShaped beam synthesis via semidefinite relaxation (SDR) based on magnitude least squares (MLS)
Author(s) -
Hu Mengkai,
Dou Xiuquan,
Liu Qifan,
Zhu Xingsheng
Publication year - 2022
Publication title -
iet microwaves, antennas and propagation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.555
H-Index - 69
eISSN - 1751-8733
pISSN - 1751-8725
DOI - 10.1049/mia2.12231
Subject(s) - dimension (graph theory) , side lobe , relaxation (psychology) , algorithm , beam (structure) , least squares function approximation , mathematics , sampling (signal processing) , mathematical optimization , variable (mathematics) , computer science , optics , mathematical analysis , telecommunications , statistics , physics , antenna (radio) , psychology , estimator , pure mathematics , social psychology , detector
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.