Tomographic Synthetic Aperture Radar (TomoSAR) has three-dimensional imaging capability. Nevertheless, its building structure is full of considerable noise and wrong targets owing to the baseline distribution and the algorithm restrictions. Accordingly, efficiently extracting and segmenting buildings from SAR point clouds with huge data is a critical issue. According to the characteristics of building facade point clouds, the KD-Tree-based Euclidean clustering is introduced as a fast-clustering method for better extracting and segmenting individual building facades. At first, a simple morphological filter is utilized to identify the ground and non-ground point clouds. The building facades are then extracted from the non-ground point clouds based on density information. Subsequently, the KD-Tree-based Euclidean clustering method denoises the building facades and segments them into individual facades. Finally, a second Euclidean clustering is performed for each of these facades to denoise them again. Experimental results indicate that the presented approach can prepare accurate and efficient extraction and segmentation results for complex urban scenes.

Since the radar imaging can serve as an ill-posed inverse problem, it is an appealing topic to introduce deep learning instead of regularized iterative algorithm to address the presence of uncertainties in the inverse synthetic aperture radar (ISAR) forward model. Usually, the obtained image quality is largely limited by the sparse representation and denoising performance of the target scene. This paper presents a deep learning technology for radar echo signal recovery with enhanced ISAR imaging capability. If the convolutional neural network (CNN) is regarded as an imaging processor, its input and output are echo data and formed image, respectively. The trained CNN contains a variety of feature extractors, which can automatically extract and abstract high-level image feature representation. The main difficulty of CNN applications in the radar imaging field, in fact, is that a large amount of appropriate training data cannot be obtained as easy as in other fields. A novel deep CNN (DCNN)-based method, U-net-based imaging, is proposed for ISAR image reconstruction using less training samples compared to existing CNN imaging networks. Some improvements are presented to make it adapt to imaging tasks. Experimental simulations are carried out to prove the remarkable performance of the proposed method. In the presence of random noise and echo undersampling, the trained U-net achieves fast and accurate reconstruction of high-quality ISAR images, respectively.

Bistatic radar possesses the forward-looking imaging performance that conventional synthetic aperture radar (SAR) lacks. Nevertheless, bistatic forward-looking imaging is seriously limited by the motion of the transmitter and receiver, resulting in a restrained resolution. In this work, the method for vortex missile-borne bistatic synthetic aperture radar (BSAR) is proposed, which combines the orbital angular momentum (OAM) beam with the traditional missile-borne BSAR technique to obtain a larger Doppler bandwidth. Higher azimuth resolution is achieved by compensating large spatial range cell migration (RCM) and serious range-azimuth coupling. Firstly, the imaging scene of vortex missile-borne BSAR and the OAM-based echo model are established and deduced. Secondly, the preprocessing method for amplitude compensation is proposed with the design of the transmitted OAM beam, which improves the uncorrelation between different pulse echoes. Then, the improved range Doppler (RD) algorithm with azimuth compensation factor is proposed for vortex missile-borne BSAR to obtain the super-resolution two-dimensional (2D) reconstruction for the target. Simulation results show that compared with conventional missile-borne BSAR, the proposed vortex missile-borne BSAR possessing the extending Doppler bandwidth can obtain superior azimuth resolution performance with the identical synthetic aperture length situation. Furthermore, this work will contribute to the application for OAM-carrying in the field of radar forward-looking imaging and the development for high-resolution missile-borne BSAR imaging technology.

Vortex electromagnetic (EM) waves carrying orbital angular momentum (OAM) have a helical phase front and a field strength with a singularity along the axial center, which is expected to obtain resolutions beyond the Rayleigh limit for imaging systems, and provide new instruments for accurate radar targets imaging. However, since the vortex EM beams feature a doughnut-like intensity profile with a central dark zone and the inconsistent divergence angles of the main lobe, it is difficult to simultaneously illuminate the target for vortex EM beams with different OAM mode, which results in limited echo energy. In addition, the restricted elevation resolution hinders the acquisition of target spatial information, which limits the development of the OAM-based radar detection technique. This contribution offers a new set of ideas and solutions for achieving the 3D ISAR imaging using the superposition of different plane spiral OAM (PSOAM) waves. First, eigenmode beam steering techniques based on radio PSOAM waves is introduced. Subsequently, the PSOAM-based ISAR imaging model for maneuvering targets is derived and the echo characteristics are analyzed. Third, the effective imaging method, based on the amplitude phase compensation and fast Tensor-SL0 reconstruction algorithm, is proposed to solve the 3D sparse imaging problem in a tensor way. Finally, simulations are carried out to verify that the proposed method not only significantly reduces the computational complexity of the image reconstruction, but also generates the 3D image with sidelobes and artifacts removed remarkably from sparse measurements.