Efficient Sparse Antenna Design Using MoM-WG: Comparative Study of Horn, Conical Horn, and Reflector Antennas by Advanced Approximations

Статья в сборнике трудов конференции

In the rapidly evolving landscape of antenna technology and engineering, there is a pressing need to continually enhance the manufacturing techniques to meet the ever-growing demands of customers. To address this challenge, researchers have embraced numerical methods to develop novel antenna designs and improve their operational efficiency. This paper focuses on investigating and comparing the efficacy of utilizing two approximation approaches in the modeling and production of sparse antennas. The fundamental idea behind these approximations is to create an optimized wire structure that maximally matches the flow of current within the antenna while maintaining its structural integrity with minimal mass. These approximations offer the advantage of reducing resource requirements and allowing for controlled accuracy in the antenna performance in subsequent simulations. In this study, we have applied these approaches to model various types of antennas, including horn, conical horn, and reflector antennas. We have examined the impact of employing these approximations on the antenna characteristics, highlighting their potential in enhancing specific features while simultaneously reducing production costs and modeling complexities. The advantages and disadvantages of these approximations are thoroughly discussed, providing valuable insights and recommendations for their practical implementation.

Библиографическая запись: Alhaj Hasan, A. F. Efficient Sparse Antenna Design Using MoM-WG: Comparative Study of Horn, Conical Horn, and Reflector Antennas by Advanced Approximations [Electronic resource] // A. F. Alhaj Hasan, M. T. Nguyen, T. R. Gazizov // Proceedings 2023 International Russian Automation Conference (RusAutoCon) (Sochi, Russian Federation, 10-16 September 2023). – New York City : IEEE, 2023. – P. 709-715. – DOI: 10.1109/RusAutoCon58002.2023.10272841

Конференция:

• 2023 International Russian Automation Conference (RusAutoCon)
• Россия, Краснодарский край, Сочи, 10-16 сентября 2023,
• Международная

Издательство:

IEEE

США, New York, New York City