Airborne array tomographic synthetic aperture radar (TomoSAR) can acquire three-dimensional (3D) information of the observed scene in a single pass. In the process of airborne array TomoSAR data imaging, due to the disturbance of factors such as inconsistent antenna patterns and baseline errors, there are spatially varying amplitude-phase inconsistency errors in the multi-channel Single-Look-Complex (SLC) images. The existence of the errors degrades the quality of the 3D imaging results, which suffer from positioning errors, stray points, and spurious targets. In this paper, a new calibration method based on multiple prominent points is proposed to calibrate the errors of amplitude-phase inconsistency. Firstly, the prominent points are selected from the multi-channel SLC data. Then, the subspace decomposition method and maximum interference spectrum method are used to extract the multi-channel amplitude-phase inconsistency information at each point. The last step is to fit the varying curve and to compensate for the errors. The performance of the method is verified using actual data. The experimental results show that compared with the traditional fixed amplitude-phase inconsistency calibration method, the proposed method can effectively calibrate spatially varying amplitude-phase inconsistency errors, thus improving on the accuracy of 3D reconstruction results for large-scale scenes.

Airborne array-interferometric synthetic aperture radar (array-InSAR), one of the implementation methods of tomographic SAR (TomoSAR), has the advantages of all-time, all-weather, high consistency, and exceptional timeliness. As urbanization continues to develop, the utilization of array-InSAR data for building detection holds significant application value. Existing methods, however, face challenges in terms of automation and detection accuracy, which can impact the subsequent accuracy and quality of building modeling. On the other hand, deep learning methods are still in their infancy in SAR point cloud processing. Existing deep learning methods do not adapt well to this problem. Therefore, we propose a Position-Feature Attention Network (PFA-Net), which seamlessly integrates positional encoding with point transformer for SAR point clouds building target segmentation tasks. Experimental results show that the proposed network is better suited to handle the inherent characteristics of SAR point clouds, including high noise levels and multiple scattering artifacts. And it achieves more accurate segmentation results while maintaining computational efficiency and avoiding errors associated with manual labeling. The experiments also investigate the role of multidimensional features in SAR point cloud data. This work also provides valuable insights and references for future research between SAR point clouds and deep learning.

Circular synthetic aperture radar (CSAR) offers multi-angle scattering for strong directional targets, benefiting urban surveying. However, the CSAR three-dimensional (3D) imaging of buildings is difficult. The reference height mismatch problem during imaging can lead to a defocused target, and a significant layover phenomenon exists in urban environments. To solve these problems and realize the CSAR 3D imaging of buildings, this study adopts the airborne circular flight array synthetic aperture radar (CFASAR) system for data acquisition and proposes a 3D processing method for airborne CFASAR based on layered phase compensation in the wavenumber domain. CFASAR has advantages over single-baseline and multi-baseline CSAR, as 3D imaging is independent of target azimuth scattering consistency and reduces flight experiment complexity. 3D imaging of buildings consists of two key steps: layered focusing based on phase compensation in the wavenumber domain and super-resolution imaging, which solves the defocusing problem and contributes to high-dimensional resolution. Compared with the traditional layered back projection (BP) imaging, the layered focusing method based on phase compensation in the wavenumber domain has a lower computational complexity. Layered focusing combined with super-resolution processing effectively suppresses conical sidelobes, addresses layover issues, and establishes an accurate 3D scattering model. The proposed method was validated using X-band airborne CFASAR data obtained in Rizhao, Shandong Province, China, in 2021. The experimental results indicate that the proposed method has low time complexity, and it can effectively suppress the conical sidelobe of CSAR and realize high-quality 3D reconstruction of buildings.

The array interferometric synthetic aperture radar (Array InSAR) system resolves shadow issues by employing two scans in opposite directions, facilitating the acquisition of a comprehensive three-dimensional representation of the observed scene. The point clouds obtained from the two scans need to be transformed into the same coordinate system using registration techniques to create a more comprehensive visual representation. However, the two-point clouds lack corresponding points and exhibit distinct geometric distortions, thereby preventing direct registration. This paper analyzes the error characteristics of array InSAR point clouds and proposes a robust registration method for array InSAR point clouds in urban scenes. It represents the 3D information of the point clouds using images, with pixel positions corresponding to the azimuth and ground range directions. Pixel intensity denotes the average height of points within the pixel. The KAZE algorithm and enhanced matching approach are used to obtain the homonymous points of two images, subsequently determining the transformation relationship between them. Experimental results with actual data demonstrate that, for architectural elements within urban scenes, the relative angular differences of registered facades are below 0.5°. As for ground elements, the Root Mean Square Error (RMSE) after registration is less than 1.5 m, thus validating the superiority of the proposed method.

Circular synthetic aperture radar (CSAR) possesses the capability of multi-angle observation, breaking through the geometric observation constraints of traditional strip SAR and holding the potential for three-dimensional imaging. Its sub-wavelength level of planar resolution, resulting from a long synthetic aperture, makes CSAR highly valuable in the field of high-precision mapping. However, the motion geometry of CSAR is more intricate compared to traditional strip SAR, demanding high precision from navigation systems. The accumulation of errors over the long synthetic aperture time cannot be overlooked. CSAR exhibits significant coupling between the range and azimuth directions, making traditional motion compensation methods based on linear SAR unsuitable for direct application in CSAR. The dynamic nature of flight, with its continuous changes in attitude, introduces a significant deformation error between the non-rigidly connected Inertial Measurement Unit (IMU) and the Global Positioning System (GPS). This deformation error makes it difficult to accurately obtain radar position information, resulting in imaging defocus. The research in this article uncovers a correlation between the deformation error and radial acceleration. Leveraging this insight, we propose utilizing radial acceleration to estimate residual motion errors. This paper delves into the analysis of Position and Orientation System (POS) errors, presenting a novel high-resolution CSAR motion compensation method based on airborne platform acceleration information. Once the system deformation parameters are calibrated using point targets, the deformation error can be directly calculated and compensated based on the acceleration information, ultimately resulting in the generation of a high-resolution image. In this paper, the effectiveness of the method is verified with airborne flight test data. This method can compensate for the deformation error and effectively improve the peak sidelobe ratio and integral sidelobe ratio of the target, thus improving image quality. The introduction of acceleration information provides new means and methods for high-resolution CSAR imaging.

Airborne array synthetic aperture radar (SAR) has made a significant breakthrough in the three-dimensional resolution of traditional SAR. In the airborne array SAR 3D imaging technology, the baseline length is the main factor restricting the resolution. Airborne array flexible SAR can increase the baseline length to improve the resolution and interference performance by mounting antennae on the wing. The existing research lacks results obtained using flexible actual data processing and specific motion compensation methods. Thus, this paper proposes a motion error estimation and compensation method for an airborne array flexible SAR based on a multi-channel interferometric phase. Firstly, a flexible channel motion compensation model is established based on the multi-channel interference phase of airborne array flexible SAR. Then, based on the rigid multi-channel data, combined with the ground control points, the least square method, and the global optimal search algorithm, the accurate rigid baseline length and the central incidence angle are obtained. Finally, according to the multi-channel interference phase inversion of the flexible motion error and combined with the motion compensation model, the flexible data are compensated in the time domain. The actual results indicate that, compared with traditional motion compensation methods, our method can obtain accurate flexible compensation data. This study improves the interference performance of multi-channel data of airborne array flexible SAR and lays a solid foundation for the high-precision 3D reconstruction of airborne array flexible SAR.