Electromagnetic Imaging of Uniaxial Objects by Artificial Intelligence Technology

Abstract

The electromagnetic imaging of uniaxial objects by the Artificial Intelligence (AI) technology is presented in this paper. We study the two-dimensional inverse scattering problem from uniaxial objects illuminated by the TM (Transverse Magnetic) and TE (Transverse Electric) polarized incident waves. As the uniaxial objects have different components of permittivity along different transverse directions, the problem of TE polarization will be more severe than that of TM polarization. We use the Dominant Current Scheme (DCS) and Back Propagation Scheme (BPS) to calculate the preliminary permittivity distribution. By combining with deep learning and neural networks, the permittivity distribution of those uniaxial objects can be reconstructed more accurately. U-Net is used to reconstruct the permittivity distribution, because U-Net has shared the weights and biases, which can effectively reduce the network complexity and is very suitable for solving image processing problems. In the numerical results, we added different noises to compare the reconstruction results of the DCS and BPS initial estimations through the U-Net. Numerical results show that the reconstruction permittivity for the DCS initial estimation is better than that for the BPS’s. Our diversity is that we have reconstructed the uniaxial objects by neural network successfully with less time-consuming effort and real-time imaging.