With the growing demand for High-Resolution and Wide-Swath (HRWS) imaging in Synthetic Aperture Radar (SAR) missions, digital beamforming (DBF) in the range dimension has become a key enabling technology. However, under wide-swath imaging conditions, existing DBF algorithms—such as SCORE, single/multi-delay DBF, multi-frequency DBF, and scaling-function DBF—often suffer from performance degradation near the swath edges, including target displacement, mainlobe broadening, and signal-to-noise ratio (SNR) reduction, which decrease imaging quality. This paper proposes an enhanced DBF algorithm based on multi-delay and scaling compensation. The proposed method introduces multi-delay correction, scaling compensation, and a data fusion strategy to effectively suppress mainlobe broadening while enhancing image SNR, all with a relatively low computational complexity ofO(N×Nr×log(Nr)). Extensive validation using both satellite simulation data and airborne measured data demonstrates the superiority of the proposed method: compared to existing algorithms, it achieves up to 6 dB SNR improvement and 0.4098 m mainlobe suppression in the near range, and up to 2 dB SNR improvement and 0.0819 m suppression in the far range. This method offers an important technical reference for efficient beam shaping and robust imaging in future wide-swath SAR systems.

As synthetic aperture radar (SAR) technology continues to evolve with a focus on miniaturization, reduced weight, and lower costs, its range of applications has broadened to encompass lightweight and slow-moving platforms, such as small autonomous aerial vehicles (AAVs) and airships. However, this advancement introduces a new challenge for SAR systems in the form of excessively long synthetic aperture time (LSAT). LSAT-SAR faces significant challenges caused by its longer integration time (10–1000 times more than conventional airborne SAR’s synthetic aperture time), including more stringent error tolerance of navigation system and more demanding trajectory control requirements. These issues often result in severe 2-D defocusing. Existing high-precision navigation systems and traditional autofocus algorithm, though adequate for conventional airborne SAR, often fail to meet the stringent requirements of LSAT-SAR, especially in low-contrast scenarios, necessitating more efficient and more robust compensation methods. To address these challenges, we propose a 2-D autofocus algorithm for LSAT-SAR using deep neural networks (DNNs). The proposed approach treats the 2-D focusing problem as a series of coupled 1-D curve estimation tasks, employing a weighted entropy loss function. The solution is optimized in an unsupervised manner using a DNN. Finally, the additional refinement is performed through a specialized fine-tuning correction module. Experiments on real LSAT-SAR images with synthetic aperture times of 150–270 s show that the proposed method substantially exceeds the performance of traditional approaches in low-contrast scenarios. It demonstrates excellent robustness, offering valuable insights and technical guidance for LSAT-SAR autofocusing.

With synthetic aperture radar (SAR) systems being developed with miniaturization, lightweight, low-cost, high-resolution, and real-time capabilities, the scope of their application has expanded to some lightweight platforms that cannot carry a high-precision position and orientation system (POS) and whose trajectories are susceptible to interference. This often results in residual range cell migration (RCM) exceeding several or even dozens of range resolution cells, which notably degrades the performance of traditional autofocus algorithms and seriously affects the quality of real-time imaging. Considering this, we propose a real-time residual RCM correction scheme based on the autocorrelated normalized cross-power spectrum (ANCPS). First, the range-compressed image is segmented into blocks based on the azimuth and range. The ANCPS is then used to estimate the optimal residual RCM for each subblock. Second, the optimal segment is selected from the residual RCM curve fragments estimated by various subblocks. Finally, the residual RCM fragments are spliced and filtered to correct the complete data. When the algorithm is deployed on a GPU platform, it only takes 1.39 s to process an$8\times 40$K high-resolution original SAR image, and the calculation time is reduced by 94.6% compared with only using a CPU for calculation. The experimental results demonstrate that the proposed residual RCM correction scheme has good convergence, can achieve subpixel residual RCM compensation without iteration and interpolation, has a simple calculation process, is efficient, and has strong parallelism. It is thus suitable for deployment in GPUs and is conducive to realizing real-time SAR autofocus at a low cost and with a high resolution.

Point clouds from real-world scenarios inevitably contain complex noise, significantly impairing the accuracy of downstream tasks. To tackle this challenge, cascading encoder-decoder architecture has become a conventional technical route to iterative denoise. However, circularly feeding the output of denoiser as its input again involves the re-extraction of underlying surface, leading to unstable denoising process and over-smoothed geometric details. To address these issues, we propose a novel denoising paradigm dubbed PD-Refiner that employs a single encoder to model the underlying surface. Then, we leverage several lightweight hierarchical Underlying Surface Inheritance Refiners (USIRs) to inherit and strengthen it, thereby avoiding the re-extraction from the intermediate point cloud. Furthermore, we design adaptive edge-aware supervision to improve the edge awareness of the USIRs, allowing for the adjustment of the denoising preferences from global structure to local details. The results demonstrate that our method not only achieves state-of-the-art performance in terms of denoising stability and efficacy, but also enhances edge clarity and point cloud uniformity.

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.

Airborne multi-antenna SAR urgently needs the high-precision motion information of each antenna to realize high-precision imaging. Distributed Position and Orientation System (POS) is an important way to solve this problem based on transfer alignment. Because multiple antennas are usually installed on the flexible and discontinuous structure, there are complex and time-varying relative motions between different antennas. This relative motion will lead to the decline of transfer alignment accuracy, and directly affects the imaging accuracy of SAR. To solve this problem, a motion information acquisition algorithm of multi-antenna SAR installed on flexible and discontinuous structure based on distributed POS is given. Firstly, the gyroscopes and accelerometers of the distributed POS are used to calculate the relative attitude between nodes, which is applicable to both continuous and discontinuous structure. Secondly, combining the smooth estimation and the distributed filter, a distributed smoothing algorithm including forward filter and backward smoothing is proposed to calculate the high-precision and smoothed motion data of each sub-node. The forward filter obtains the motion parameters of each sub-node through transfer alignment based on the above relative attitude data. The backward smoothing further improves the motion information accuracy of sub-node by using the measurement data at all time and sharing the data of all nodes. Through the flight imaging test of multi-antenna SAR installed on a flexible and discontinuous platform, it is shown that the proposed algorithm can significantly improve the accuracy of the sub-node motion data, in which the errors of position, velocity and attitude are reduced by 58.9%, 61.9% and 71.2% respectively. Based on this motion data, SAR achieves better imaging focusing effect.

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades.

Array Tomographic Synthetic Aperture Radar (TomoSAR) is an advanced technique with elevation resolution through acquiring images from different angles. Due to the special single-pass mode, there is no temporal decorrelation caused by revisit time between multiple images. Therefore, the accuracy of the elevation reconstruction of the observed scene can be improved, especially in mountain area. In this paper, a framework to achieve 3D reconstruction in mountain area for Array TomoSAR is proposed. Furthermore, the complex overlays and terrain continuity are fully considered. To validate the effectiveness of the proposed framework, the experimental results are compared to SRTM-1 data obtained by Shuttle Radar Topography Mission (SRTM). Also, the original 2D image with scattering intensity is utilized.

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.

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.

Advanced computer architectures rely mainly on compiler optimizations for parallelization, vectorization, and pipelining. Efficient-code generation is based on a control dependence analysis to find the basic blocks and to determine the regions of control. However, unstructured branch statements, such as jumps and goto's, render the control flow analysis difficult, time-consuming, and result in poor code generation. Branches are part of many programming languages and occur in legacy and maintenance code as well as in assembler, intermediate languages, and byte code. A simple and effective technique is presented to convert unstructured branches into hammock graph control structures. Using three basic transformations, an equivalent program is obtained in which all control statements have a well-defined scope. In the interest of predication and branch prediction, the number of control variables has been minimized, thereby allowing a limited code replication. The correctness of the transformations has been proven using an axiomatic proof rule system. With respect to previous work, the algorithm is simpler and the branch conditions are less complex, making the program more readable and the code generation more efficient. Additionally, hammock graphs define single entry single exit regions and therefore allow localized optimizations. The restructuring method has been implemented into the parallelizing compiler FPT and allows to extract parallelism in unstructured programs. The use of hammock graph transformations in other application areas such as vectorization, decompilation, and assembly program restructuring is also demonstrated.

Program parallelization is inhibited by unstructured control statements such as GOTOs, causing interacting and overlapping execution trajectories. In this contribution, a program restructuring method is proposed to convert unstructured control statements into block if statements and while loops. Furthermore, an algorithm is presented to transform a common type of while loops into do loops. The technique works for while loops of which the control variables satisfy a linear recurrence relation. As a result, the loop carried dependencies generated by the control variables are removed. If there are no other loop carried dependencies, the do loop may then be converted into a doall loop. The algorithm has been used to test and convert a significant number of while loops into doall loops for a suite of well-known numerical benchmarks.