The Adjustment Analysis Method of the Active Surface Antenna Based on Convolutional Neural Network

Abstract

Active surface technique is one of the key technologies to ensure the reflector accuracy of the millimeter/submillimeter wave large reflector antenna. The antenna is complex, large-scale, and high-precision equipment, and its active surfaces are affected by various factors that are difficult to comprehensively deal with. In this paper, based on the advantage of the deep learning method that can be improved through data learning, we propose the active adjustment value analysis method of large reflector antenna based on deep learning. This method constructs a neural network model for antenna active adjustment analysis in view of the fact that a large reflector antenna consists of multiple panels spliced together. Based on the constraint that a single actuator has to support multiple panels (usually 4), an autonomously learned neural network emphasis layer module is designed to enhance the adaptability of the active adjustment neural network model. The classical 8-meter antenna is used as a case study, the actuators have a mean adjustment error of 0.00252 mm, and the corresponding antenna surface error is 0.00523 mm. This active adjustment result shows the effectiveness of the method in this paper.