Without phase information of the measured field data, the phaseless data inverse scattering problems (PD-ISPs) counter more serious nonlinearity and ill-posedness compared to full data ISPs (FD-ISPs). In this paper, we propose a learning-based inversion approach in the frame of the U-net convolutional neural network (CNN) to quantitatively image unknown scatterers located in homogeneous background from amplitude-only measured total field (also denoted phaseless data). Three training schemes with different inputs to the U-net CNN are proposed and compared, i.e., the direct inversions (DIS) scheme with phaseless total field data, retrieval dominant induced currents by Levenberg-Marquardt (LM) method (PD-DICs) and phaseless data with contrast source inversion scheme (PD-CSI). We also demonstrate the setup of training data and compare the performance of the three schemes using both numerical and experimental tests. It is found that the proposed PD-CSI and PD-DICs perform better in terms of accuracy, generalization ability and robustness compared to DIS. PD-CSI has the strongest capability to tackle with PD-ISPs, which outperforms the PDDICs and DIS.

This paper proposes a learning-based non-iterative method to solve electromagnetic (EM) scattering problems utilizing pix2pix, a popular generative adversarial network (GAN). Instead of calculating induced currents directly from a matrix inversion, a forward induced current learning method (FICLM) is introduced to calculate the induced current through a neural-network mapping. The scattered fields can be further calculated through a multiplication of the Green’s function with the predicted induced currents. Inspired by wave physics of scattering problems, we have designed three kinds of input schemes, covering different combinations of the given incident field and permittivity contrast, to evaluate the performance of the FICLM model under both single-incidence and multi-incidence cases. Numerical results prove that the proposed FICLM outperforms the method of moments (MoM) in terms of both computational speed and accuracy by use of reference data with a higher precision. The FICLM with the direct sum of permittivity contrast and a so-called Born-type induced current, achieves the best calculation accuracy and generalization capability compared to the other two inputs. The comparison with other types of neural networks, such as U-net, also demonstrates the superior performance of FICLM for dealing with complex scatterers due to the use of adversarial framework in pix2pix. The proposed method paves a new way for the fast solution of EM scattering problems through deep learning techniques.

Deep learning based inverse scattering (DL-IS) methods attract much attention in recent years due to advantages of fast speed and high-quality reconstruction. The loss functions of neural networks in DL-IS methods are commonly based on a pixel-wise mean squared error (MSE) between the reconstructed image and its reference one. In this paper, we introduce a structural similarity (SSIM) loss function to combine with the MSE loss for reconstructing dielectric targets under a DL-IS framework. The SSIM loss imposes a further regularization on the target at the perceptual level. Numerical tests for both synthetic and experimental data verify that this new perceptually-inspired loss function can effectively improve the imaging quality and the generalization capability of the trained model.

A deep learning-based inversion algorithm is developed to solve the inhomogeneous background inverse scattering problem (ISP). To alleviate the burden of nonlinearity and ill-posedness of the ISP, a noniterative method called the distorted-Born backpropagation scheme is introduced to quantitatively reconstruct a rough image of the unknown object in inhomogeneous background. The roughly reconstructed result serves as the input of the designed generative adversarial network (GAN), which outputs the fine reconstructed image of the relative permittivity. The generator network of the GAN is well designed as an encoder-decoder structure configured with the attention scheme. The discriminator network is taken to supervise the generator to learn the features of target scatterers through an adversarial training process. The proposed method is proven to be effective in reconstructing objects embedded in inhomogeneous background, which promises a real-time application future of the ISP.

A planar periodic leaky-wave antenna (PLWA) with continuous backward-to-forward beam-scanning capability and consistent gain is presented. The PLWA is made of a new type of periodically arranging meander line structures, in which the capacitive coupling between neighboring units plays the key role in achieving a high scanning rate. In detail, the beam-scanning rate of the proposed PLWA is efficiently tuned by varying the gap between two adjacent units, without considerably changing the working frequency band. To mitigate the open stopband (OSB) and to improve the impedance matching, several inductive open stubs are introduced into a single meander line unit cell to enhance the radiation. In the experiment, a prototype of the proposed PLWA was fabricated and measured. The simulated and measured beams show good agreement in terms of scanning range and radiation performance. A continuous beam scanning from -60° to +58° through broadside in the frequency band of 5.95-7.1 GHz is observed, and hence, a 102.6°/GHz scanning rate is realized in practice. Besides, the PLWA shows an average gain level of 11.96 dBi with a variation lower than 2 dB and a sidelobe below -10 dB at all the measured frequencies. The proposed PLWA may have potential applications in radar, microwave imaging, and wireless communication due to its compact structure, easy to fabricate, and dispersionless performance.