Open AccessSynthesis of a Sparse 2D-Scanning Array using Particle Swarm Optimization for Side-Lobe Reduction
Author(s) -
Sivaranjan Goswami,
Kandarpa Kumar Sarma,
Kumaresh Sarmah
Publication year - 2021
Publication title -
wseas transactions on communications/wseas transactions on communications
Language(s) - English
Resource type - Journals
eISSN - 2224-2864
pISSN - 1109-2742
DOI - 10.37394/23204.2021.20.14
Subject(s) - side lobe , particle swarm optimization , antenna array , sparse array , phased array , hadamard transform , matrix (chemical analysis) , range (aeronautics) , antenna (radio) , reduction (mathematics) , azimuth , computer science , artificial neural network , algorithm , mathematics , optics , materials science , physics , telecommunications , artificial intelligence , mathematical analysis , geometry , composite material
Synthesis of sparse arrays is a promising area of research for a wide range of applications including radar and millimeter-wave wireless communication. The design goal of array thinning problems is to reduce the number of elements of an array without significantly affecting its performance. This work presents a technique for synthesizing a sparse phased-array antenna from a 16×16 uniform rectangular array (URA). The proposed approach reduces the number of elements by 50% without any significant increase in the peak sidelobe level (PSLL) for all possible scan angles in the azimuthal and elevation plans within a finite range of scan angles. The synthesis includes an artificial neural network (ANN) model for estimation of the excitation weights of the URA for a given scan-angle. The weights of the sparse array are computed by the Hadamard product of the weight matrix of the URA with a binary matrix that is obtained using particle swarm optimization (PSO) to minimize the PSLL.