Near-Field Prediction in Complex Environment Based on Phaseless Scanned Fields and Machine Learning
Jun Wen, Xing‐Chang Wei, Yongliang Zhang, Tian‐Hao Song
Abstract
The field prediction of an unknown electromagnetic interference (EMI) source within a complex electromagnetic environment requires complex radiation field formulas. In this article, we propose an artificial neural network (ANN) method to predict the field by using the scanned phaseless near-field from the EMI source. The near-field magnitude is scanned using a near-field probe first. After that, an ANN is trained to present the mapping between the observation point and the radiation field. A set of free-space Green's functions are used as the input of the ANN, and the magnitude of the radiation field is taken as the output of the ANN. With the help of Green's function, the trained ANN can accurately predict the radiation field outside the scanning regions. The feasibility of this method is verified using numerical and measurement experiments. The proposed method can realize source reconstruction in a complex electromagnetic environment.