Antenna Near-Field Reconstruction from Far-Field Data Using Convolutional Neural Networks
It addresses antenna diagnostics and electromagnetic interference analysis, but is incremental as it applies an existing deep learning method to a specific domain problem.
This paper tackled the problem of reconstructing near-field distributions from far-field antenna data using Convolutional Neural Networks, achieving a test mean squared error of 0.3898.
Electromagnetic field reconstruction is crucial in many applications, including antenna diagnostics, electromagnetic interference analysis, and system modeling. This paper presents a deep learning-based approach for Far-Field to Near-Field (FF-NF) transformation using Convolutional Neural Networks (CNNs). The goal is to reconstruct near-field distributions from the far-field data of an antenna without relying on explicit analytical transformations. The CNNs are trained on paired far-field and near-field data and evaluated using mean squared error (MSE). The best model achieves a training error of 0.0199 and a test error of 0.3898. Moreover, visual comparisons between the predicted and true near-field distributions demonstrate the model's effectiveness in capturing complex electromagnetic field behavior, highlighting the potential of deep learning in electromagnetic field reconstruction.