In this article, we present a novel synthetic aperture radar (SAR) 3-D imaging strategy using circular Interferometric SAR (InSAR) data. Our approach improves upon the Holographic SAR tomography (HoloSAR) techniques by eliminating the need for multi-baseline data collection nor residual motion error correction over a long curvilinear aperture. This may provide a simple yet effective 3-D imaging solution for perturbed airborne radar platforms. The key innovation is the first utilization of multi-aspect SAR interferograms to invert the 3-D or 4-D (3-D spatial coordinates (x, y, z) and radar azimuth angles θ) scattering power distribution of the imaged scene. Our fundamental assumption is that the imaged scene conforms approximately to a random volume scattering model. Thus, we can use the projection-slice theorem to establish a mathematical relationship between the multi-look interferograms and the scene’s 3-D/4-D scattering power distribution. The fundamental concepts and resolution theory of this new 3-D inversion strategy are developed in 3-D K-space using Fourier aperture synthesis theories. Then, we designed two algorithms for reconstructing a 3-D image: a filtered back-projection algorithm for reconstructing isotropic targets, and a compressed sensing imaging method for reconstructing large-scale targets with anisotropic behaviors. Finally, we verified the feasibility of proposed methods through experiments in real airborne scenarios.

Multi-aspect SAR can obtain more backscatter information about the target by observing targets from different azimuths by means of radar. More and more attention has been paid to the analysis of typical target characteristics in SAR scenes. The target characteristics mainly include the anisotropy and isotropy characteristics of the target. In the process of multi-aspect analysis, SAR images from different angles are routinely used to analyze typical targets in the area. In this article, the SAR images of different angles are filled into the matrix for characteristic analysis. Secondly, the full-angle data is a 360-degree coherent image of the scene. For the established matrix, we randomly remove some data to reverse the target characteristics. Calculate the feature matrix under constraint conditions by combining the matrix with the collaborative filtering algorithm, and finally get the predicted value of the missing angle. C-band circular SAR data is used to validate our method.

In recent years, multi-angular SAR is widely researched, including wide angle SAR and circular SAR. These kinds of SAR working modes can detect the aspect dependent scattering characteristic of the target. However, most of the researches are concerned about the amplitude information. The multi-angular phase characteristic is also an important and useful information that can be obtained from the raw data or the image. In this paper, the multi-angular phase characteristic of canonical structures is analyzed by electromagnetic simulation. Then a man-made target detection method is proposed based on the multi-angular phase characteristic. The method is validated by an X-band SAR chamber data and the GOTCHA X-band circualr SAR data. The result preliminarily shows the ability of phase on analyzing the anisotropic scattering.

Multi-angular SAR can be used to obtain the information of target scattering characteristics at different aspect angles. The scattering anisotropy extraction attracts more attention but the method is not much. And because of background noise, the anisotropy extraction based on aspect entropy is not good. Low-rank matrix decomposition is widely used in the change detection process of SAR images. The most important thing is that it can distinguish the strong point target from the background image and the sparse matrix obtained by decomposition eliminates the sidelobe noise of the target. In this paper, firstly we proposed the application of low-rank matrix decomposition to multi-angular SAR images to analyze the scattering characteristics and then the coefficient of variation is used to validate and quantify the anisotropy of the target in the scene after low-rank matrix decomposition. The anisotropy quantization result is less affected by noise than aspect entropy. The Gotcha X-band circular SAR data is used to validate the method.1

Reconstructing a three-dimensional (3-D) target model from a collection of multi-aspect SAR images has been a hotspot. When imaged from different viewing aspect angles, a 3-D target will project onto different 2-D locations and present different geometric shapes. Echoes from the targets generate the non-background areas in the SAR imagery, so the geometry of the background areas can be used to restrict the possible 3-D shapes and positions of the targets. In this paper, we develop a background constraint for checking the consistency between 3-D models and the subaperture image sequence. Then a 3-D reconstruction algorithm using single-pass circular SAR (CSAR) imagery is proposed. The algorithm iteratively removes the incompatible voxels from an initial 3-D model by checking the constraint-consistency of each illuminated voxel in the model until the model converges. Also, we use a ray tracing strategy to check whether a voxel can be illuminated by the radar, so the proposed algorithm can robustly deal with the shadow effects in SAR images. The performance of the algorithm is validated using the C-band CSAR imagery acquired by the Aerospace Information Research Institute, Chinese Academy of Sciences (AIRCAS).

In conventional synthetic aperture radar (SAR) working mode, targets are assumed isotropic due to the limited aperture length. However, most of man-made targets are anisotropic. Therefore, the anisotropic scattering can help us do man-made target detection. Circular SAR (CSAR) [1] is a new SAR working mode and it can obtain the anisotropic scattering of the target by 360° observation. In this paper, the multi-angular statistical properties of targets are analyzed. The probability density functions (PDF) of the anisotropic target are various under different aspect viewing angles, while the PDFs of the isotropic target are basically stable. Then a man-made target detection method is proposed based on the multi-angular statistical property. Likelihood ratio test [2] is used to judge whether the statistical property of scattering is anisotropic or isotropic. Then anisotropic scatterings, which represent the man-made targets, can be discriminated from isotropic scatterings by thresholding. An X-band chamber circular SAR data and a C-band airborne circular SAR data are used to illustrated our idea.

The scattering of an anisotropic target is aspect dependent. Circular SAR (CSAR) can observe the scattering behavior in different aspect angles. In this paper, we propose an anisotropy scattering analysis method based on the likelihood ratio using CSAR data. CSAR data is used to provide sub-aperture images in different aspect angles. The likelihood ratio is defined as the ratio of the conditional probability under two hypotheses, anisotropic and isotropic. Anisotropic and isotropic scatterings can be discriminated by the value of the likelihood ratio. The scattering direction of the anisotropic scattering can be obtained by using our method too. We use a C-band CSAR data, which is acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS) to validate our method.

Circular Synthetic Aperture Radar(CSAR) has become a hotspot with its characteristic of elevation plane resolution and all-aspect observing ability. Digital elevation model (DEM) extraction in urban arears by using single-pass CSAR data without requiring additional knowledge is a subject of interest. The target, whose real height is not equal to the reference imaging height will project to different locations after imaging in different sub-aperture. In this paper, the quantitative relationship between offset of imaging points and height difference is deduced theoretically in the real scene, where the airborne SAR platform trajectory is not a standard circle. DEM of an area is presented using the data acquired by the Institute of Electronics, Chinese Academy of Sciences (IECAS). Compared with the DEM provided by the German Aerospace Center (DLR) with 1m absolute height error, the effectiveness of the proposed method is verified.