Deformation Localization of Reflector Antenna Based on Focal-Field Distribution with CapsNet

Nanjie Lv, Decheng Wu, H. Cao, Lisheng Yang, Jin Fan
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Abstract

An artificial intelligence-assisted reflector antenna deformation localization method based on the focal-field distribution (FFD) feature is presented. Exploring the characteristics of FFD, a capsule network (CapsNet)-based estimation method is used to locate the deformation. The Five-hundred-meter Aperture Spherical radio Telescope (FAST) model is used to verify the effectiveness of the proposed method, and the experimental results confirm that our method can accurately locate the deformation of the reflector antenna.
基于CapsNet的反射面天线焦场分布变形定位
提出了一种基于焦场分布特征的人工智能辅助反射面天线变形定位方法。探索FFD的特点,采用基于胶囊网络(CapsNet)的估计方法对变形进行定位。利用500米口径球面射电望远镜(FAST)模型验证了该方法的有效性,实验结果证实了该方法能够准确定位反射面天线的变形。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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