Conformal Antenna Array Pattern Synthesis Using Genetic Learning Particle Swarm Optimization Algorithm
Abstract
Investigations on conformal phased array pattern synthesis using genetic learning particle swarm optimization algorithm (GL-PSO) are presented in this paper. The hybrid algorithm is composed of two cascading layers, the first for exemplar generation and the second for particle update. Genetic operators are used to generate exemplars from which particles learn and, in turn, historical search information of particles provides guidance to the evolution of the exemplars. The hybrid algorithm is applied to synthesize radiation pattern of a 4×2 cylindrical conformal microstrip antenna .The excitation weights of the conformal array elements are optimized to obtain specific radiation pattern including scanning angle, reduced SLL, limited beamwidth. The influence of mutual coupling and conformal platform are fully considered in the optimization process. Experimental results have verified that the GL-PSO has fast convergence speed and high convergence accuracy when applied to antenna array pattern synthesis.
Keywords
Conformal antenna array, Pattern synthesis, Genetic algorithm, Particle swarm optimization
DOI
10.12783/dtetr/ecar2018/26380
10.12783/dtetr/ecar2018/26380
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