Hybrid dilated convolutional neural network for solving electromagnetic inverse scattering problems

Abstract

In this article, a novel machine learning network is presented for solving 2-D electromagnetic (EM) inverse scattering problems (ISPs). The conventional approach of solving ISPs may suffer some difficulties such as the intrinsic strong nonlinearity, ill-conditioned problem, and high computational cost, especially when the scatterer is electrically large or contains high dielectric contrast. In order to solve the above problems, a novel hybrid dilated convolutional neural network (HDCNN) is proposed, which integrates with the advantage of the dilated convolution operation and the downsampling-upsampling (DSUS) algorithm. On one hand, the dilated convolution can expand the reception field valiantly and reduce the depth of neural network without extra computational cost. On the other hand, the DSUS algorithm can effectively overcome the irrelevance of the long-range data, when the dilated convolution is utilized. In addition, to avoid gridding problems, the HDCNN contains three dilated convolution layers with a different dilation rate, instead of adopting the same dimensional convolution kernel. Meanwhile, the proposed HDCNN could directly address the obtained scattering electric field without extra pretreatment. After executing the HDCNN process, EM parameters, for example, the relative permittivity, can be fast reconstructed. The efficiency and validity of the network can be demonstrated by four numerical results.