@inproceedings{cbe1d63accc643d3ae9ccff4a1d0c45d,
title = "Deep Learning Based Efficient Beam Steering Algorithm for Deformed Curved Array Antenna",
abstract = "In this paper, a physically driven deep learning method is proposed for the beam steering problem of deformed conformal array antenna. The loss design is carried out by three constraints: gain, main lobe direction and side lobe level (SLL), and the softmax method is chosen for the loss design in order to solve the gradient truncation problem in the neural network. The specific direction map is obtained by active element pattern (AEP) algorithm after the output amplitude and phase of the neural network. The results show that the trained neural network output performs well under three constraints, and the gain and SLL obtained by this method are better than the superior single constraint.",
keywords = "active element pattern, beam steering, component, deep learning, deformed conformal array antenna",
author = "Zhaoming Han and Hongwei Gao and Cheng Jin and Xuejiao Zhao",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024 ; Conference date: 16-08-2024 Through 19-08-2024",
year = "2024",
doi = "10.1109/ACES-China62474.2024.10699995",
language = "English",
series = "2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024 - Proceedings",
address = "United States",
}