Circular SAR Imaging Research Articles

A GAN-based fast focusing method for circular SAR images

Circular Synthetic Aperture Radar(CSAR) imaging is vulnerable to perturbations in the atmosphere and various other elements that can lead to position offset errors in the antenna's phase center as well as induce motion errors. Traditional phase compensation methods that operate in the time domain, such as Auto-regressive Back-projection (ARBP), typically require computation on a direction-by-direction basis, which can result in the considerable expenditure of time and memory resources. To address these challenges, thispaperintroduces a novel approach for focusing on CSAR images. This method leverages the training of a Generative Adversarial Network (GAN) to directly achieve focus on CSAR sub-aperture images. Additionally, to counteract the network's tendency towards low-frequency preferences, the Auto-focus Frequency Loss (AFFL) is introduced. Moreover, to enhance the accuracy of focus position extraction, the Focus Position Feature Attention (FPFA) is proposed. These innovations, along with a new fusion strategy for the sub-aperture images post-focusing, have been experimentally validated, demonstrating significant improvements in the efficiency and accuracy of CSAR image focusing.

Terahertz Circular SAR Imaging Algorithm Based on the Extraction of Scattering Characteristics of Target Structures

Terahertz Circular SAR Imaging Algorithm Based on the Extraction of Scattering Characteristics of Target Structures

3-D Tomographic Circular SAR Imaging of Targets Using Scattering Phase Correction

3-D Tomographic Circular SAR Imaging of Targets Using Scattering Phase Correction

A novel method for extracting geometric parameter information of buildings based on CSAR images

ABSTRACT Compared with the traditional linear synthetic aperture radar (LSAR), circular SAR (CSAR) system can observe 360° of the observation area. In turn, more abundant scattering information in all directions of the observation area can be obtained, making it easier to observe the target information. Based on the observation advantages of CSAR, a new method for extracting geometric parameters of buildings is proposed in this paper. In this method, the scattering features of buildings on CSAR sub-aperture images and full-aperture images are extracted, and the corresponding relationship between the scattering features and building geometric parameters is deduced, and then the building geometric parameters are calculated. Through the above corresponding relationship, the geometric parameters of the building are calculated. At the same time, in this method, the advantages of CSAR omnidirectional imaging are used to extract the geometric parameters of buildings from multiple sub-aperture images. This method realizes the high-precision extraction of building geometric parameters by CSAR imaging for the first time. Finally, the experimental verification is carried out by introducing the measured data of the Ku-band autonomously measured, which confirms the effectiveness and accuracy of the proposed method.

Multi-Layered Circular Dielectric Structures’ Synthetic Aperture Radar Imaging Based on Green’s Function Using Non-Uniform Measurements

The electromagnetic imaging of multi-layered circular structures can be accomplished by means of different approaches. Among these approaches, the use of synthetic aperture radar (SAR) techniques with the compensation of the Green’s function for multi-layered circular cylinders provides a trade-off between accuracy and computational complexity. Nevertheless, this approach relies on measurement points acquired uniformly. Thus, this prevents the adoption of more flexible sampling schemes, limiting the use of inaccurate positioners or manual scanners. To overcome this limitation, this paper proposes a method to handle non-uniform cylindrically acquired data, enabling multi-layered circular imaging based on non-uniformly acquired measurements. For this purpose, the presented method starts by projecting the non-uniformly measured points onto different circles with equally-spaced points. After that, the Green’s function-based circular-SAR imaging method is applied to each projection circle. Lastly, the final SAR image is obtained by the superposition of the images from the previous step. Numerical simulations and experimental results demonstrated the effectiveness and robustness of the proposed method for practical nondestructive testing (NDT) applications.

Circular SAR imaging algorithm based on polar format algorithm for moving target

For stationary targets, circular synthetic aperture radar (CSAR) imaging has tended to be mature and perfect. However, for moving targets, due to the long synthetic aperture and the variation of slant plane with the observation angle, the motion compensation is more complicated and has no effective approach. Based on the latest moving target trajectory reconstruction method in CSAR (2015), the polar format algorithm can be improved to image the moving targets when the motion parameters have been estimated. First, the authors placed the imaging reference point on the target to track it, and then compensated for the phase error caused by the incident angle changing and the wavefront curvature. The simulation results of point show that the algorithm is effective and accurate.

Retrieval of Polarimetric Azimuthal Angular Characteristics via the Application of Target Decomposition to Spectral Domain Circular SAR Images

In this paper, we discuss the application of polarimetric target decomposition (TD) theorems to circular synthetic aperture radar (CSAR) images to retrieve the polarimetric azimuthal angular characteristics of the targets. In CSAR systems, radar-carrying aircraft moves along a circular path. The antenna beam is pointed toward the center of the circular path during the data acquisition to irradiate the spotlighted area from various azimuthal or aspect angles. Therefore, the resultant polarimetric CSAR images contain information about the polarimetric scattering mechanisms of the targets at each aspect angles. To recover this information, we propose the use of spectral decomposition of CSAR images in combination with several TD algorithms. The spectral domain CSAR images can be viewed as the azimuthal angular spectrum images. Thus, the application of TD algorithms to the spectral domain images is expected to be able to obtain the angular dependence and polarimetric scattering properties of the targets simultaneously. We carried out a simple numerical simulation and an indoor laboratory experiment to validate the proposed TD scheme.

Resolution-Based Analysis for Optimizing Subaperture Measurements in Circular SAR Imaging

Circular geometries are often encountered in nondestructive testing and evaluation applications. Circular synthetic aperture radar imaging technique, capable of producing high-resolution images, has great utility for this purpose. However, in many such applications, scanning only a portion of the circular geometry (i.e., scanning domain), called the subaperture, may only be possible or sufficient for a particular inspection need. Consequently, in subaperture imaging, for an imaging aspect angle and aspect angle measurement interval, we must determine the expected image resolution. In this paper, for an arbitrary area of interest, we analyze the image spatial resolution obtained from a generally defined aspect angle and aspect angle measurement interval. Using resolution as the criteria, we use this method to determine the boundary between small and large subapertures, which also show to produce markedly different range and cross-range resolutions as a function of aspect angle and aspect angle measurement interval. Moreover, the optimum aspect angle and aspect angle measurement interval for this fixed area of interest are defined based on these analyses. Finally, the results of these analyses and images obtained from simulations are compared and corroborated with relevant experimental results at X-band (8.2–12.4 GHz).

An Improved H/<inline-formula> <tex-math notation="LaTeX">$\alpha$ </tex-math> </inline-formula> Unsupervised Classification Method for Circular PolSAR Images

In conventional polarimetric synthetic aperture radar, targets are usually assumed isotropic, and potential polarimetric variations in azimuth are ignored. As to polarimetric circular SAR (CSAR), the azimuthal aperture is much larger, and polarimetric variations in azimuth are no longer negligible. Moreover, whether a target has changed polarimetric properties in azimuth, i.e., anisotropic, is an important feature. $H/\alpha $ classification scheme is a famous and effective unsupervised classification method. However, when applying $H/\alpha $ classification scheme against polarimetric CSAR data, problems occur. First, during the formation of a large aperture, polarimetric properties from different angles of view are combined, which affects the estimation of $H$ and $\alpha $ . Second, $H/\alpha $ method cannot distinguish anisotropic and isotropic targets. In this paper, a pixel-wise $H/\alpha $ calculation method and an unsupervised classification scheme against polarimetric CSAR images are proposed to solve the two problems. With the pixel-wise $H/\alpha $ calculation method, $H$ and $\alpha $ are more accurately calculated. Meanwhile, anisotropic and isotropic targets which have same scattering mechanism can be distinguished by the proposed classification method. The effectiveness of the pixel-wise $H/\alpha $ calculation method is demonstrated by both the simulated and real data. The unsupervised classification method is demonstrated based on real data, acquired by airborne CSAR system at $P$ -band, the Institute of Electronics, Chinese Academy of Sciences, Beijing, China.

Fast Factorized Backprojection Algorithm for One-Stationary Bistatic Spotlight Circular SAR Image Formation

In this paper, a fast factorized backprojection (FFBP) algorithm is proposed for the one-stationary bistatic spotlight circular synthetic aperture radar (OS-BSCSAR) data processing. This method represents the subimages on polar grids in the slant-range plane instead of the ground plane, which can be accurately referenced to the tracks of both transmitter and receiver. It can not only accurately accommodate the complicated circular track including the motion error, scene topographic information, large spatial variances and significant range-azimuth coupling of the echo data, but also improve the imaging efficiency compared with the backprojection (BP) algorithm. First, OS-BSCSAR imaging geometry is provided, and then the bistatic BP algorithm for the OS-BSCSAR imaging is derived to provide a basis for the proposed FFBP algorithm. Second, based on the subaperture imaging model, the polar grids for calculating the subimages are defined, and the sampling requirements for the polar grids are derived from the viewpoint of the bandwidth, which can offer the near-optimum tradeoff between the imaging quality and the imaging efficiency. Finally, implementation and computational burden of the proposed FFBP algorithm is discussed, and then the speed-up factor of the proposed FFBP algorithm with respect to the bistatic BP algorithm is derived. Experiment results are given to prove the correctness of the theory analysis and validity of the proposed FFBP algorithm.

Adaptive imaging of anisotropic target based on circular‐SAR

The isotropic reflectivity assumption is a valid approximation in traditional narrow-angle synthetic aperture radar (SAR). However, for large angular ranges most targets exhibit only limited scattering persistence. Therefore, this assumption is violated in circular-SAR (CSAR). Thus, the performance of traditional fully coherent image formation is poor in CSAR, because coherent integration over the entire aperture will obscure aspect-independent reflectivity characteristics. To address this problem, an adaptive imaging algorithm which accommodates anisotropic targets to improve image quality is proposed. The efficient use of valid azimuth angles can reduce noise and errors and highlight anisotropic targets in CSAR image. Another advantage of this algorithm is that the anisotropy target scattering behaviour can be extracted by data inversion in the imaging process. Compared with the sub-aperture approach, which can also extract the anisotropic scattering properties of target, the data inversion approach in the algorithm can provide more accurate results and a finer curve. The adaptive imaging method is validated in this Letter by simulation and an airborne dataset.

Airborne DLSLA 3-D SAR Image Reconstruction by Combination of Polar Formatting and <inline-formula> <tex-math notation="LaTeX">$L_1$</tex-math></inline-formula> Regularization

Airborne downward-looking sparse linear array 3-D synthetic aperture radar (DLSLA 3-D SAR) operates downward-looking observation and obtains the 3-D microwave scattering information of the observed scene. The cross-track physical sparse linear array is often configured to obtain uniform virtual phase centers in order to adopt the frequency-domain algorithm. However, the virtual phase centers usually have to be nonuniformly and sparsely distributed due to the array elements' installation locations restricted by the airborne platform and the airborne wing tremor effect. In this state, the frequency-domain algorithm cannot be directly used. In this paper, a DLSLA 3-D SAR image reconstruction algorithm that combines polar formatting and $L_1$ regularization is presented. Wave propagation and along-track dimensional imaging are first finished after polar formatting and wavefront curvature phase error compensation; then, cross-track dimensional imaging is completed with the $L_1$ regularization technique. The proposed algorithm is applicable to airborne DLSLA 3-D SAR imaging under nonuniformly and sparsely distributed virtual phase centers condition. The proposed algorithm was verified by 3-D distributed scene simulation experiment (P-band circular SAR image was selected as radar cross-section input, and X-band digital elevation model of the same area was selected as the coordinate positions of the scene) and the field experiment. Image reconstruction results and image reconstruction performances, such as normalized radar cross section, height errors, and orthographic projection image grayscale distribution, are demonstrated and analyzed with different signal-to-noise ratios, different array sparsity, and the incomplete compensated residual oscillation error 3-D distributed scene simulation experiments. Simulation and field experimental results show the good performance in focusing and the robustness of the proposed algorithm.

Vehicle Layover Removal in Circular SAR Images via ROSL

Circular synthetic aperture radar (CSAR) has raised interest in both wide-aspect-angle 2-D imaging and 3-D feature reconstruction. As for CSAR 2-D imaging with a 360° aperture, vehicles spotlighted in the scene are depicted with multiview layover, which makes the imagery intuitively less comprehensible. In addition, the shape of the layover bulge depends on the elevation of the radar platform. Thus, otherwise identical vehicle targets appear differently in the image when the elevation deviates from a constant, which makes target discrimination more difficult. In this letter, subaperture images are vectorized and stacked to build a composite matrix. Decomposing the composite matrix via robust orthonormal subspace learning results in a low-rank matrix and a sparse matrix. The layover belongs to the sparse matrix and thus can be get rid of. The performance of the proposed method has been verified on synthesized and real CSAR data sets. Experimental results show that the multiview layover of vehicles is eliminated effectively. Moreover, the CSAR images become insensitive to elevation variation after layover removal, which benefits target discrimination.

Statistical Models of Speckle for Circular SAR Imaging

Statistical Models of Speckle for Circular SAR Imaging

Expediting back‐projection algorithm for circular SAR imaging

The availability of an expediting back-projection (EBP) algorithm for circular synthetic aperture radar (CSAR) imaging is investigated. EBP works by dividing the full aperture into sub-apertures, and using a unified polar grid to generate sub-images. On the basis of the correspondence between the wavenumber domain and the phase history domain, EBP combines all sub-aperture spectrums into a full-aperture one and reconstructs the CSAR image followed by a two-dimensional (2D) fast Fourier transform. Without time-consuming 2D interpolation, EBP is capable of decreasing the loss of spectrum, while improving significantly the computational efficiency compared with direct BP.

Wide‐field circular SAR imaging: An empirical assessment of layover effects

ABSTRACTIn circular synthetic aperture radar (CSAR), targets are usually scanned over the complete azimuthal aperture of 360°. A major challenge inhibiting successful CSAR image production is encountered in two‐dimensional imaging of relatively large scenes (i.e. wide‐field scenarios). For such cases, the spatially variant radar signature gives rises to image degradations known as layover effects. In this paper, the imaging results for hypothetical and real complex targets using their CSAR data at C band are demonstrated to characterize these effects. In real experiments, a ground‐based, outdoor CSAR system is exploited thanks to the circular balcony of a building that provides 270° view of targets. A spherical back‐projection algorithm is used to focus near‐field data and the reconstructed images are also cleaned out from clutter to more easily visualize the layover artifacts. © 2015 Wiley Periodicals, Inc. Microwave Opt Technol Lett 57:489–497, 2015

Polar Format Imaging Algorithm With Wave-Front Curvature Phase Error Compensation for Airborne DLSLA Three-Dimensional SAR

Airborne downward-looking sparse linear array 3-D synthetic aperture radar operates nadir observation and obtains the 3-D microwave scatter information of the observed scene. A polar format algorithm (PFA) with space-variant wave-front curvature phase error compensation is presented. A 3-D image in polar coordinate can be obtained with the proposed PFA, and a 3-D image in Cartesian coordinate can be obtained with interpolation. The proposed PFA possesses the advantages of high precision, low memory requirement, and low computational complexity. The focus performance of the proposed PFA is validated by 3-D distributed scene simulation with an airborne X-band digital elevation model and a P-band circular SAR image of the same area as simulation scene input.

Airborne Downward Looking Sparse Linear Array 3-D SAR Heterogeneous Parallel Simulation

The airborne downward looking sparse linear array three dimensional synthetic aperture radar (DLSLA 3-D SAR) operates nadir observation with the along-track synthetic aperture formulated by platform movement and the cross-track synthetic aperture formulated by physical sparse linear array. Considering the lack of DLSLA 3-D SAR data in the current preliminary study stage, it is very important and essential to develop DLSLA 3-D SAR simulation (echo generation simulation and image reconstruction simulation, including point targets simulation and 3-D distributed scene simulation). In this paper, DLSLA 3-D SAR imaging geometry, the echo signal model and the heterogeneous parallel technique are discussed first. Then, heterogeneous parallel echo generation simulation with time domain correlation and the frequency domain correlation method is described. In the following, heterogeneous parallel image reconstruction simulation with two imaging algorithms, e.g., 3-D polar format algorithm, polar formatting and L1 regularization algorithm is discussed. Finally, the point targets and the 3-D distributed scene simulation are demonstrated to validate the effectiveness and performance of our proposed heterogeneous parallel simulation technique. The 3-D distributed scene employs airborne X-band DEM and P-band Circular SAR image of the same area as simulation scene input.

Effects on Three-Dimensional Geosynchronous Circular SAR Imaging by Orbit Errors

Circular SAR imaging on the geosynchronous orbit is an innovative SAR imaging mode, which can perform high resolution three-dimensional (3D) imaging with continuous observations for a large area. However, for geosynchronous satellites the orbit is severely disturbed by perturbing effects, and the orbit error will be an important factor influencing the SAR imaging on the geosynchronous orbit. In this paper, the effects of the orbit error on performances of 3D geosynchronous circular SAR (GEOCSAR) imaging are analyzed. The error model is firstly made, and then the orbit error propagating into the distance from the radar to the target (range) is derived. Furthermore, the effects of different components of the range error on the quality of focused SAR signals are deduced, based on the 3D GEOCSAR signal model with time-domain correlation algorithm. The analyses and simulations will quantify and verify the required accuracies of GEOCSAR orbit estimation for ensuring the 3D imaging quality.

A novel modified Omega-K algorithm for circular trajectory scanning SAR imaging using series reversion

This article presents a modified Omega-K algorithm for circular trajectory scanning synthetic aperture radar (CTSSAR) imaging. Due to the curvature of circular trajectory, it is difficult to have access to the two-dimensional frequency spectrum for CTSSAR via the principle of stationary phase (POSP), as conventional SAR imaging methods RD and CS. Herein, the analytic point target spectrum is first derived by series reversion and the POSP, based on which a modified Omega-K algorithm is developed to focus data accurately. The accuracy can be controlled by keeping enough terms in the two series expansions so that a well-focused image can be achieved with a proper range approximation. After detailed analyses and experiments, the fourth-order approximation is proved to be the best choice. Furthermore, the computational efficiency is evaluated by comparing the given method with the back projection algorithm and other methods with different approximated orders. The proposed algorithm is verified to be the best one in terms of computational burden. A well-focused image is obtained by simulations, validating the feasibility of the proposed algorithm.