Optimization of a 36-Element Broadband Direction-Finding Antenna Array Using Printed Vivaldi Array Elements with Extended Flares

Abstract

In this study, we proposed a 36-element broadband array antenna optimization method using printed Vivaldi antennas with an extended aperture. The direction-finding performance was compared to a conventional rectangular array to verify the optimum configuration. The proposed array configuration with ideal dipole elements demonstrated an RMSE less than 1° and an SLL greater than 4.1 dB in all frequency bands when the signal was incident at Az=50°. In addition, a printed Vivaldi antenna with an extended conductive flare was designed and fabricated as an array element to provide broadband characteristics and a wide beamwidth. Finally, a direction-finding simulation was performed considering the array manifold of the optimal array configuration with the proposed array element, and the results of the RMSE and SLL were less than 1.2° and greater than 6.7 dB, respectively.

Design of a Bent Wire Monopole Antenna Using Machine Learning

“…그러나 안테나 성능 에 민감하게 영향을 주는 설계변수가 많을 경우, 시행착 오법만을 사용해서는 안테나 구조를 최적화하기 어렵다. 이러한 시행착오법의 제약점을 극복하기 위해서 유전자 알고리즘(genetic algorithm: GA)과 입자 군집 최적화 기법 (particle swarm optimization: PSO) 등과 같은 최적화 기법 이 소개되었다 [7]∼ [9] . 그러나 유전자 알고리즘을 활용한 최적화 기법이나 입자군집 최적화 기법을 안테나 최적설 계에 활용할 경우, 안테나의 성능을 확인하기 위한 수치 해석이 수반되기 때문에 많은 시간적, 비용적 노력이 요 구된다.…”