An Electromagnetic Imaging Algorithm Based on Generative Adversarial Network for Limited Observation Angle

In the context of long-distance detection and obstacle occlusion, the limited observation angle of electromagnetic imaging poses significant challenges for accurate reconstruction. To address this issue, we propose a hybrid electromagnetic reconstruction algorithm based on a generative adversarial network (GAN). This algorithm utilizes the diffraction tomography (DT) method to generate an initial image, which serves as input for the GAN. Through adversarial training between the generator and the discriminator, the algorithm produces a reconstructed image with enhanced accuracy. Firstly, unlike complete learning-based reconstruction methods that rely solely on scattering field data, our approach effectively integrates both scattering characteristics and a priori information from the DT image model, thus improving the accuracy and generalizability of the neural network. Secondly, compared to other linear approximation algorithms, the DT algorithm incorporates fast Fourier transform (FFT) to enhance computational efficiency. Thirdly, this study employs a Fourier spatial data extrapolation technique to mitigate the limitations of insufficient data and improve imaging fidelity. Numerical simulations demonstrate that even at a narrow observation angle of 900, the proposed algorithm exhibits excellent reconstruction performance and notable generalization ability.