The vortex electromagnetic (EM) wave carrying orbital angular momentum (OAM) offers a new degree of freedom for radar techniques, although to date its application to synthetic aperture radar (SAR) imaging has not yet been widely reported. In this paper, a SAR imaging model based on vortex EM waves is derived. Ku-band vehicle-borne vortex EM waves SAR experiment using phased uniform circular array (UCA) antennas is reported for the first time. A detailed comparison with conventional plane EM waves SAR imaging is given in terms of signal-to-noise ratio, and azimuth resolution. The experimental results demonstrate the effectiveness of the proposed imaging model, and indicate that vortex EM waves can be exploited in SAR imaging with higher azimuth resolution than plane EM waves. OAM, which is completely independent of frequency and polarization, is a new degree of freedom in SAR imaging. This work shows the new applications of vortex EM waves, which paves the way for the development of novel SAR systems.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can straightly obtain the 3-D imagery of the illuminated scene with the single-pass flight. Generally, the Rayleigh resolution of the elevation direction is unacceptable due to the length limitation of linear array. The super-resolution imaging algorithms within the compressive sensing (CS) framework have been extensively studied because of the essential spatial sparsity in the elevation direction. However, the super-resolution performance of the existing sparse reconstruction algorithms will deteriorate dramatically in the case of lower signal-to-noise ratio (SNR) level or a few antenna elements. To overcome this problem, a new super-resolution imaging structure based on CS and deep neural network (DNN) for MIMO array SAR is proposed in this article. In this new algorithm, the spatial filtering based on CS is first proposed to reserve the signals only impinging from the prespecified space subregions. Thereafter, a group of parallel end-to-end DNN regression models are designed for mapping the potential sparse recovery mathematical model and further locating the true scatterers in the elevation direction. Finally, extensive simulations and airborne MIMO array SAR experiments are investigated to validate that the proposed method can realize the state-of-the-art super-resolution imaging against other existing related methods.

Recent advances in very high-resolution tomographic synthetic aperture radar (SAR) inversion using multiple data stacks from different viewing angles enable us to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. They can be potentially used for facade reconstruction and deformation monitoring in an urban area. When SAR tomographic processing is used to form 3-D point clouds, some points that we simply omitted in the traditional 3-D reconstruction are difficult to understand. It comes to our attention that this special detail which seems violated to a common practice is due to the mechanism of multipath (MP) scattering. Dominated by SAR tomography process, MP scattering reveals many interesting phenomena in 3-D point clouds. In this paper, a theoretical model of the MP scattering of the structure of dihedral corner which often appears in the urban area is given. There are two groups of points distributed symmetrically and compactly around the base angle of the building. The positions of these two groups of the points can be predicted by our model. Corresponding experiments are carried out to show the validity. The position is relative to the height of the building which indicates a direct utilization of our model to estimate the height of the building directly without 3-D reconstruction.

It has become a field of intensive research to exploit SAR tomography to reconstruct a 3-D model of the buildings. However, in multiple-input multiple-output (MIMO) SAR tomography, the double-bounce reflections of the building corner will cause symmetric virtual scatterers, affecting both the scattering coefficient and structure of the 3-D model. To solve this problem, the building corner reflection is discussed and compressive sensing (CS)-based corner reflection suppression (CSCRS) is proposed. In the final part of this letter, the effectiveness of the proposed method is validated using array InSAR data. It is found that the proposed method leads to considerable improvements with regard to suppression ratio and reconstruction accuracy.

The problem of synthesis scatterers in inverse synthetic aperture radar (ISAR) make it difficult to realize high-resolution three-dimensional (3D) imaging. Radar array provides an available solution to this problem, but the resolution is restricted by limited aperture size and number of antennas, leading to deterioration of the 3D imaging performance. To solve these problems, we propose a novel 3D imaging method with an array ISAR system based on sparse Bayesian inference. First, the 3D imaging model using a sparse linear array is introduced. Then the elastic net estimation and Bayesian information criterion are introduced to fulfill model order selection automatically. Finally, the sparse Bayesian inference is adopted to realize super-resolution imaging and to get the 3D image of target of interest. The proposed method is used to process real radar data of a Ku band array ISAR system. The results show that the proposed method can effectively solve the problem of synthesis scatterers and realize super-resolution 3D imaging, which verify the practicality of our proposed method.

Special attention has been devoted to joint wireless communication and radar sensing concepts over the past decades. Considerable confusion arose, however, with regard to waveform designs. In this paper, various joint wireless communication and radar sensing waveform schemes are reviewed and classified into two categories. Disadvantages and limitations of each category are clarified theoretically. It is shown that waveforms designed merely in conventional time and frequency domains cannot fulfill the conflict requirements posed by radar and wireless communication. As a solution to this fundamental challenge, the multidimensional waveform concepts, along with a generalized correlation model, are proposed for joint wireless communication and radar sensing. To showcase our current research advances in this emerging area, several concrete waveforms designed in multiple dimensions are introduced. Theoretical analysis is validated by simulations and outfield demonstration experiments. It is shown that the suggested waveforms can simultaneously achieve superior detection and communication performances with shared spectrum resources.