SAR Tomography can provide three-dimensional (3D) image of the observed scenes, becoming an important technology for urban mapping, target detecting and many other fields. When imaging urban scenes, multipath signals are always considered as noise in the previous research on 3D SAR imaging. However, the signals experienced multiple bounces hold massive information about the target scenario. In this paper, we aim to turn "waste" into treasure by digging for the hidden information in multipath. Based on the TomoSAR mechanism, we complete the restoration of the 3D imaging result. Firstly, we introduce the TomoSAR imaging model under the low-altitude case, which is more suitable for airborne or UAV- borne platform in urban area. We analyze the multipath model and propose the multipath detection and restoration methods. The virtual targets caused by multipath can be restored to the correct positions in the image by relocating. To verify the validity of our method, we conducted a series of real-data experiments, the results show that our method makes full use of multipath signals to retrieve non-line-of-sight targets missing in traditional direct-path 3D imaging.

The rapid development in SAR three-dimensional imaging technology and the feasibility of employing lightweight and convenient platforms have increased the attention of low-altitude UAV-borne TomoSAR. However, the building obstructions in urban area and the low altitude cause multipath phenomenon, which is challenging in imaging interpretation and accurate reconstruction. While previous research is mainly about the removal of multipath effects, we focus on exploiting multipath information within the framework of low-altitude TomoSAR. This paper aims to explore effective strategies for determining the viability of multipath exploitation in urban canyons. Firstly, we introduce the low-altitude TomoSAR imaging model under the spherical wavefront model. Through a comprehensive analysis of the range profile of the multipath mechanism, our objective is to provide detection and mechanism determination before relocating or restoring the multipath for TomoSAR. This algorithm was applied in real-data experiments to investigate the multipath mechanisms of different cases, validating practical value.

With the development of synthetic aperture radar (SAR) 3-D imaging technology, urban 3-D imaging has been realized by unmanned aerial vehicle (UAV)-borne array interferometric SAR (array-InSAR), which enables simpler access to SAR data. However, in low-altitude SAR 3-D imaging of urban scenes, multipath effect is significant. In past studies, multipath signals have been mostly considered as a hindrance in interpretation, but they provide important clues for imaging hidden targets in non-line-of-sight (NLOS) regions. Based on this idea, this article studies the multipath exploitation in low-altitude UAV-borne array-InSAR 3-D imaging and realizes accurate imaging of NLOS targets. An improved 3-D imaging model suitable for low-altitude cases is introduced to achieve preliminary 3-D imaging. Subsequently, key planes are extracted from the original results and the 3-D multipath reachable area is analyzed. After obtaining the multipath mechanism of the NLOS targets, they can eventually be accurately relocated and imaged. Both the simulation and real-data experiments show that the proposed method can effectively 3-D imaging and relocate NLOS targets in urban canyons, with errors typically below 0.5 m. The imageable range in the direction of the ground range can be expanded by 41.62%, and the imageable 3-D area can be expanded by 76.57%, which realizes the NLOS information acquisition.

Non-line-of-sight (NLOS) target imaging is a challenging issue in Synthetic Aperture Radar (SAR), because obstacles may block the direct line of sight between the radar and the target. In SAR tomographic (TomoSAR)3D imaging, the wrong presence of NLOS points can impact the accuracy and interpretability of the results. This paper presents an approach that not only detects but also relocates NLOS targets to their actual positions. The method relies on multipath signal analysis within a geometric model. The simulation and experimental results on a drone-borne system with 4 channels demonstrate that the proposed approach can effectively image NLOS targets, and has the potential for practical applications.

SAR tomography (TomoSAR) has the three-dimensional (3-D) resolving ability. Most existing 3D imaging methods for TomoSAR consider the layovers for different channels are the same. However, in the low-altitude airborne cases, the variation of layovers between channels becomes unneglectable. In this letter, we studied the channel migration phenomenon of sparse TomoSAR under low-altitude scenarios. We derived the model of the differential range from a particular target to different antennas, which is nearly a linear function along the array dimension. Therefore, we applied Keystone Transform on the array axis to correct this channel migration, and then the traditional compressed sensing-based 3D imaging methods for TomoSAR can be applied to get the final 3D reconstruction results. The approach was tested with simulation data and also the real data of the MV3DSAR system, which is a mini drone-borne TomoSAR system. The results demonstrate that the proposed method can provide a more accurate solution to the low-altitude sparse TomoSAR reconstruction problem.