Hexagonal circularly polarized antenna array and rapid optimization method thereof

Abstract

The invention relates to a hexagonal circularly polarized antenna array and a rapid optimization method thereof. The hexagonal circularly polarized antenna array comprises an FR4 medium substrate, a feed network, a reflective face and a hexagonal radiation patch. The rapid optimization method of the hexagonal circularly polarized antenna array includes the following steps that 1, a finite elementmode of a unit antenna to be optimized is established; 2, the design variable of the unit antenna to be optimized is determined; 3, a parallel lower confidence limit optimization algorithm is called to optimize the finite element model of the initial unit antenna; 4, an optimizing result Y= (y1, y2,..., yn) T, and an optimal design scheme Yopt is analyzed and utilized for arraying, so that a finite element model of an initial array to be optimized is obtained; 5, the design variable of the finite element model of the initial array to be optimized is determined and serves as the initial samplepoint of the parallel lower confidence limit algorithm; 6, the parallel lower confidence limit algorithm is called to optimize the initial array to be optimized; 7, the optimization result of the antenna array is obtained.

Description

technical field [0001] The invention relates to a hexagonal circularly polarized antenna array and a fast optimization method thereof, belonging to the technical field of electromagnetic fields. Background technique [0002] With the development of communication technology and the Internet of Things, various communication systems have higher and higher requirements for antennas and array antennas in terms of bandwidth, gain, size, and polarization characteristics. The resulting structure, design complexity, and parameter The diversity of the antenna and the multiplicity of the above requirements make the design of the antenna need to introduce a high-dimensional multi-objective optimization algorithm. The existing optimization design methods are very dependent on the designer's experience, and when there are many parameters and more requirements on the results, the optimization algorithm is easy to fall into local optimum. [0003] Traditional optimization algorithms such a...

AI Technical Summary

Problems solved by technology

Existing optimization design methods are very dependent on the experience of the designer, and when there are many parameters and more requirements on the results, the optimization algorithm is easy to fall into local optimum

[0003]Traditional optimization algorithms such as "Li Jun. Application of Genetic Algorithms in Antenna Design [J]. Mobile Communications, 2000,24(5):41-43.DOI: 10.3969 / j.issn.1006-1010.2000.05.009.", the genetic algorithm is a very classic global optimization algorithm, its principle is simple, it is very suitable for the optimization of the antenna structure, but when there are many optimization parameters When , its convergence speed is slow and the efficiency is low, so the traditional global optimization algorithm has certain limitations

Images