Wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) has garnered attention due to its capability to monitor a stationary area over an extended period and ability of tracking moving targets. Recently, the research on moving target trajectory reconstruction has also raised interest. However, the low positioning accuracy and impractical assumption of traditional method which makes it can hardly practical application. We propose a novel precise trajectory reconstruction of moving target in this paper. First, we segment the echo into subapertures, after clutter rejection and radial velocity estimation, quasi-maximum likelihood (QML) is applied to estimate polynomial phase signal (PPS) parameters of phase history. And then, Levenberg-Marquardt (LM) algorithm is applied to decouple the motion parameters. Based on the motion parameters, the coarse trajectory can be reconstructed. After that, the envelope history of moving target is extracted. Modeling the motion trajectory of the moving target in segments and estimate the trajectory of each segment using the corrected envelope history and coarse trajectory. Finally, after processing all segments, the precise trajectory of moving target is obtained. The effectiveness and practicability of the proposed algorithm were demonstrated by the processing results of experimental WasSAR data.

The Wide-Angle Stare Synthetic Aperture Radar (WasSAR), as an emerging SAR observation mode, enables long-duration, multi-angular imaging of a scene, demonstrating remarkable advantages in three-dimensional (3D) information acquisition. A critical step in the process of 3D information extraction lies in accurately determining the displacement information between sub-aperture images captured from adjacent azimuth angles. During the WasSAR imaging process, spatial targets exhibit positional discrepancies in imaging results obtained from different azimuth perspectives, making pixel-wise displacement estimation in SAR images highly challenging, especially in complex scenarios. To address this challenge, this study proposes an innovative displacement estimation method for WasSAR imagery tailored for 3D information extraction. The proposed approach begins with brightness equalization preprocessing to harmonize the brightness distribution between two images, ensuring the accuracy of subsequent processing steps. This is followed by an initial estimation using block-based registration techniques based on the Enhanced Correlation Coefficient (ECC). Finally, an improved optical flow algorithm is employed to achieve precise displacement estimation, significantly enhancing the accuracy and reliability of displacement information estimation between SAR images. A series of experiments were conducted using Ku-band Mountain scene datasets acquired by the National University of Defense Technology. The experimental results include a comprehensive comparative analysis with several existing displacement estimation methods, showcasing the superior performance of the proposed algorithm across multiple key performance metrics. The proposed algorithm’s performance in 3D information extraction applications was also evaluated, confirming its effectiveness and high practicality.

Synthetic aperture radar (SAR) multiview observations can acquire omnidirectional scattering characteristics of targets, providing richer information for target detection and recognition. Deep learning has been widely applied to SAR image interpretation in recent years. However, the sensitivity of SAR images to imaging parameters and the limited training samples pose challenges in applying deep learning to SAR target detection. Therefore, this article first designs an SAR omnidirectional subaperture division data enhancement method with different aspect accumulation angles, multilook numbers, and subaperture overlap angles based on SAR imaging parameters and the original echo. Meanwhile, an omnidirectional SAR target detection dataset called SAR-Vehicle-Det is constructed using this subaperture division method to evaluate the model performance. Then, this article proposes a novel network attributed scattering center (ASC)-U2Det based on ASC guidance for SAR target detection. The prediction is divided into the ASC reconstruction image branch and the target detection branch. The reconstruction branch uses ASC reconstruction mask maps to guide the network to extract the SAR target scattering features at different aspect angles, suppress the background clutter interference, and improve the detection accuracy of the target detection branch. Finally, this article also evaluates the impact of imaging parameters with different subaperture accumulation angles and multilook numbers on the network detection performance. Experiments on the SAR-Vehicle-Det and miniSAR datasets show that the proposed ASC-U2Det outperforms many existing target detection algorithms.

In order to adapt to various detection environments on the ground, airborne synthetic aperture radar (SAR) as a remote sensing platform usually arcs along nonlinear trajectories, and the large accumulation angle in the circling process also improves the imaging effect. However, the arc motion demands rigorous control of the flying platform and precise measurement of the motion. In some cases, there is a significant discrepancy between the track recorded by the flight platform and the actual track, which not only affects the imaging effect but also interferes with the positioning and navigation of the platform. This article presents a new method of arc-line SAR positioning and imaging based on a closed-loop structure. The echo history extracted from a 1-D range profile is corrected using an echo-history correction factor (EHCF), which reduces the self-positioning error of the platform caused by the motion measurement device. This allows for accurate positioning of the flying platform and the acquisition of imaging results with superior focusing performance. The effectiveness and stability of the proposed method are proven by simulation and experimental results.

Circular synthetic aperture radar (CSAR) has garnered a lot of attention because of its exceptional capabilities. However, CSAR imaging is sensitive to terrain undulation errors due to the curved trajectory. The projection of an object whose height deviates from a reference height (RH) forms a circular ring in the full-aperture image. Presently, error compensation using a digital elevation model (DEM) is a prevalent solution to this issue. Although DEM can be inverted from echo data, the accuracy is unsatisfactory. Inspired by optical multi-focus image fusion methods, a CSAR multi-layer focusing imaging method is proposed in this letter. Firstly, CSAR sub-aperture images are used to construct multi-layer focusing images by displacement compensation, which is more efficient than applying an imaging algorithm directly. Secondly, the multi-focus image fusion method is applied to obtain a fully focusing image. Finally, the decision maps are used to invert the target region’s DEM. The multi-layer focusing imaging method can make objects of different heights focused. The fused result is fully focused and has a resolution of 1 m, depending on the CSAR system. The inverted DEM can visually describe the objects’ contours. Experimental results demonstrate the effectiveness and practicability of the proposed method.

A unique mode known as wide-angle staring synthetic aperture radar ground moving target indication (WasSAR-GMTI) allows for the dynamic surveillance of moving targets over a wide range of azimuth angles. Despite WasSAR-GMTI develops rapidly, the parameter estimation and trajectory reconstruction of moving target in a three-Dimensional (3-D) field have not been solved well in WasSAR. In order to address this problem, a framework based on 3-D velocities’ and 3-D positions’ estimation is proposed in this article. On account of the derived equivalence, it is possible to characterize moving targets in WasSAR-GMTI and achieve the decoupling of velocity and position. First, for the multidimensional velocities’ estimation, the joint interferometric phases of several subapertures are utilized. Then, to estimate the multidimensional positions, the signal of moving target in WasSAR is modeled as a polynomial-phase signal (PPS). We tackle the issue of 3-D positions’ estimation through the coefficients of PPS estimated by cubic phase function (CPF) method. Finally, 3-D trajectory reconstruction of moving target is accomplished by combining the results of multiaperture estimation. The proposed method extends the parameter estimation to the 3-D case, and the applications of WasSAR-GMTI are spread. Moreover, the parameter estimation of moving target is addressed independently, namely without auxiliary information of priori road detail. The estimation accuracy of the proposed method is evaluated by the simulated data, and the validity and feasibility of the proposed method are demonstrated through the results of real data.

The acquisition of 3-D information in the observation scene has always been a prominent issue in the field of synthetic aperture radar (SAR). The emerging wide-angle staring SAR (WasSAR) utilizes its unique multiview observation performance to obtain offset information of the target position under different observation angles, enabling the acquisition of 3-D information of the observation scene. However, existing multiview 3-D information extraction methods suffer from large errors due to a lack of prior information about the observed scene. In order to address these challenges, this article analyzes the impact of the missing incidence angle information on the 3-D reconstruction results and proposes a 3-D information correction algorithm. The method only needs the single-channel echo information of the two azimuth angles of a single pass and the corresponding radar platform motion information and does not need to rely on any a priori information of the observation scene, which realizes the acquisition of 3-D information without a priori information in the real sense. Through simulation experiments, we quantitatively analyze the error transfer coefficient and confirm both accuracy and effectiveness through experimental data processing in Ku-band mountain scenes autonomously measured by our team. The proposed algorithm significantly enhances measurement precision and reliability, demonstrating a mean error reduction to just 49% and a root-mean-square error (RMSE) decrease to 46% compared to the traditional method, thereby confirming its superior performance and practicality.

The low-band synthetic aperture radar (SAR) is a system that can present foliage-penetrating (FP) targets and exposed strong-scattering targets while the high-band SAR system can present exposed targets and texture information except for FP targets. Therefore, the different features of co-registered low-band and high-band SAR images can theoretically reveal FP targets with some disturbances. However, the co-registered dual-band SAR images cannot do the difference operator directly for the distinct statistical property of the SAR image pair. The traditional method is to transfer the background texture from the high-band SAR image to the low-band SAR image by the cycle-consistent generative adversarial network (CGAN) method. However, CGAN would generate discontinuity and mistakes in transferring the large-scale texture information, and the complex network would heavily increase the time complexity of the application. To overcome this issue, we first innovatively introduce a fast and accurate texture-transferring method based on the dual-band SAR image fusion (DBIF), then we further combine the DBIF-based texture-transferring method with the difference operator and the threshold segmentation, and finally we get a novel DBIF-based FP target detection (DBIFFPTD) method. To verify the feasibility of the DBIF-based texture transfer method, we discuss the reflection form of different landscape objects at dual-band waves and estimate the theoretical texture transferring the image. Experiments on the open dual-band AIR-MD-SAR dataset and the independently measured dual-band SAR dataset show that the novel DBIF performs better than CGAN in transferring texture information of dual-band SAR images. Besides, dual-band linear SAR (LSAR) and circular SAR (CSAR) FP experiments are both conducted to show that the proposed DBIFFPTD has wide applicability in the flight track and is superior to the traditional low-band double-parameter constant false alarm rate (DCFAR) detector-based FPTD (DCFARFPTD) method and the dual-band CGAN-based FPTD (CGANFPTD) method.

The performance of multi-channel synthetic aperture radar (SAR)-based ground moving target indication (GMTI) is inevitably restricted by channel imperfections and imbalances, and channel calibration procedure is incorporated into the scheme accordingly. The adaptive two-dimensional calibration (A2DC) algorithm is sensitive to outliers and suffers from correction errors. To solve the problem, a modified A2DC (MA2DC) algorithm is proposed in this letter. By improving Doppler calibration model, the proposed MA2DC is more robust than the A2DC algorithm. It can effectively suppress clutter while maintaining detailed features of moving targets, after which the detection capability of moving targets in SAR-GMTI is improved. Its performance is demonstrated by the Gotcha data, which is publicly released by Air Force Research Laboratory.

Road extraction is an important part of synthetic aperture radar (SAR) image interpretation. In recent years, circular SAR (CSAR) has attracted extensive attention from researchers owing to its ability of 360° observation. Due to the unique imaging geometry of CSAR, CSAR images contain more complete road information. However, the curvilinear-structured appearance of roads in CSAR images and the complexity of the scene result in difficulties in road extraction. The curvilinear structure detector (CSD) is capable of extracting the curvilinear structures with a specific width from complicated image scenes. Based on the traditional CSD, an improved CSD (ICSD) for local road area extraction from CSAR images is introduced in this article. First, by adopting ICSD, the edges of a CSAR image and centerlines of local roads are extracted, as well as their direction. Second, the local roads are obtained by geometrical and radiometrical rules. Finally, the missing intersections and edge pixels are repaired to acquire high-precision and high-quality extraction results of the local road area. The experimental results on different band CSAR images reveal that the proposed method exhibit enhanced performance than the three state-of-the-art methods.

For synthetic aperture radar (SAR) images, there are many defocused moving targets with azimuthal deviation. Ground moving target indication (GMTI) is developing rapidly to detect moving target for traffic surveillance and military vehicle tracking. In this paper, multi-channel SAR-GMTI technology is studied and a moving target detection method based on clutter suppression interference (CSI) and relaxation-based cyclic (RELAX) is proposed to detect moving target efficiently and accurately, the time of which is only 0.22% of the time by global RELAX. Combined with multi-channel signals, CSI is performed to detect targets of interest. Then, RELAX is applied to further reduce false alarm and estimate radial velocity for geolocation. Experimental SAR data processing results show that the method is effective for moving target detection and parameter estimation.

Since the circular track synthetic aperture radar (CSAR) could observe the target scene in all directions, it can perform three-dimensional imaging of the target area. When using the sub-aperture sequence to extract the digital elevation model (DEM) of the target scene, in order to be able to reasonably use the correlation between the sub-apertures in the arc to improve DEM extraction accuracy, this paper proposes a method to extract DEM information using the correlation between sub-apertures. By using the feature of stronger correlation between adjacent sub-apertures, the accuracy of DEM extraction is effectively improved. Finally, it was verified by quoting the measured data to verify the feasibility and accuracy of the algorithm.

Circular synthetic aperture radar (CSAR) observes the scene by 360 degrees, the characteristics of targets from different aspects can be obtained. The noncoherent imaging is more popular since more textural features and lower speckle noise can be acquired. However, due to the motion error of the radar platform and the topography of observation scene, the noncoherent imaging result of full aperture can not be derived by simply noncoherent superimposing the subaperture images. In this paper, based on the enhanced correlation coefficient (ECC), we proposed a registration strategy to obtain full aperture noncoherent imaging result of CSAR, and the experiments demonstrate the effectiveness of our method.

Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results.

Since the trajectory deviations of a radar platform cause serious phase errors that degrade the focusing quality of synthetic aperture radar (SAR) imagery, an autofocus method is very important for high-resolution airborne SAR imaging. In this letter, an extended autofocus backprojection (EABP) algorithm is developed to accommodate the phase errors. Under the criterion of maximum image sharpness, the traditional ABP algorithm supports a broader class of collection and imaging geometries. However, it neglects the influence of SAR image energy distribution on the estimation of phase errors that make it inapplicable for SAR imaging, which has high dynamic range, such as low-frequency SAR imaging. By choosing regions and balancing the energy distribution of the data, the EABP algorithm is more efficient and useful to avoid the estimation error caused by the unbalanced energy distribution. Its performance has been demonstrated by using the experimental data that are acquired by a P-band airborne SAR system with a low-accuracy global positioning system.

In the last few years, interest in circular synthetic aperture radar (CSAR) acquisitions has arisen as a consequence of the potential achievement of 3D reconstructions over 360° azimuth angle variation. In real-world scenarios, full 3D reconstructions of arbitrary targets need multi-pass data, which makes the processing complex, money-consuming, and time expending. In this paper, we propose a processing strategy for the 3D reconstruction of vehicle, which can avoid using multi-pass data by introducing a priori information of vehicle's shape. Besides, the proposed strategy just needs the single-pass single-polarization CSAR data to perform vehicle's 3D reconstruction, which makes the processing much more economic and efficient. First, an analysis of the distribution of attributed scattering centers from vehicle facet model is presented. And the analysis results show that a smooth and continuous basic outline of vehicle could be extracted from the peak curve of a noncoherent processing image. Second, the 3D location of vehicle roofline is inferred from layover with empirical insets of the basic outline. At last, the basic line and roofline of the vehicle are used to estimate the vehicle's 3D information and constitute the vehicle's 3D outline. The simulated and measured data processing results prove the correctness and effectiveness of our proposed strategy.