This letter proposes a novel self-calibration method for channel phase error (CPE) in antenna arrays by leveraging the geometric properties of non-collinear configurations. The CPE can be decomposed into linear and orthogonal components relative to the baseline length vector, which cause direction-of-arrival (DOA) bias and manifold distortion, respectively. However, for self-calibration methods, only the manifold distortion can be perceived and corrected. In collinear arrays, the DOA bias is independent of manifold distortion, so the linear component of CPE can't be self-calibrated. Fortunately, in non-collinear arrays, we can couple the DOA bias into the manifold distortion by reformulating the slant range formula with the Fresnel and virtual collinear array (VCA) approximations, making it possible to estimate the entire CPE without external references. We then propose an iterative least-squares algorithm that corrects for CPE using multiple snapshots collected from a non-collinear array. Simulations under various SNRs, distances, linear components of CPE, degrees of non-collinearity, and fields of view demonstrate the effectiveness and robustness of the proposed method.

To address the critical challenge of simultaneous interpretation for hybrid scattering targets in complex urban environments, this paper proposes a novel polarimetric information-based three-dimensional (3D) imaging framework. By integrating polarimetric decomposition with morphological operations, this framework achieves preliminary classification between dense manmade regions and natural terrains. For the layover of manmade targets, we propose a polarization-based joint sparse method. It can pre-judge the dominant scatterer within pixel blocks and selectively extract optimal polarization channels to facilitate layover separation by leveraging differences across various polarization channels. For distributed natural terrains, we employ a spectral analysis method based on polarimetric covariance matrix (CM) estimation. It extracts non-local homogeneous pixel blocks within the polarization-Euclidean space through the synergistic integration of scattering mechanism measurements and statistical elevation priors, thereby enabling CM and elevation estimation. Subsequently, from both simulated and real-data experiments, the proposed framework has been validated effective in enhancing reconstruction accuracy and completeness through optimal utilization of polarization information.

This paper introduced a three-dimensional reconstruction approach for Synthetic Aperture Radar (SAR) based on polarization-coherent optimization. It includes two-dimensional imaging of full-polarization tomographic SAR data, along with registration and amplitude-phase correction of the resultant images. Through the application of a polarization-coherent optimal strategy, a set of coherent-optimal two-dimensional complex images is derived for the full-polarization multi-channel tomographic SAR. Subsequently, a three-dimensional reconstruction is executed using a compressive sensing technique, yielding a refined three-dimensional point cloud of the target region.This innovation addresses challenges encountered in traditional single-polarization tomographic SAR systems, including issues such as low image coherence, subpar three-dimensional reconstruction quality, and inadequate detail in point cloud representation. By employing the polarization-coherent optimal method on two-dimensional complex images from multi-channel full-polarization tomographic SAR, a set of images with optimal coherence coefficients is obtained. This approach effectively enhances coherence among two-dimensional complex images of each channel in full-polarization TomoSAR, ultimately elevating the quality of three-dimensional imaging point clouds.The methodology involves initial two-dimensional imaging of full-polarization multi-channel tomographic SAR echo data, followed by registration and amplitude-phase correction of the obtained complex image set. The polarization-coherent optimal method is then applied to acquire a set of coherent-optimal two-dimensional complex images. These images are further processed using a compressive sensing-based three-dimensional reconstruction algorithm, yielding the original three-dimensional point cloud. Finally, post-processing steps such as point cloud filtering and coordinate transformation are applied to obtain a high-quality three-dimensional SAR image of the target area.The significance of this approach is demonstrated through its application in the field of Polarimetric TomoSAR (PolTomoSAR), a vital tool for acquiring three-dimensional information about urban structures. The proposed method addresses the challenges faced by PolTomoSAR, including the limited application of full-polarization data and the need to improve coherence among multi-channel complex images for effective three-dimensional reconstruction.Additionally, the effectiveness of the proposed method is validated through the acquisition and processing of real-world data from a fully polarized P-band TomoSAR radar system over the Dunhuang Quadrilateral residential area. The developed technique holds great potential in advancing the applications of polarimetric SAR data in three-dimensional imaging, urban mapping, city planning, and building monitoring.

Synthetic Aperture Radar Tomography (TomoSAR) thre-dimensional imaging technology is built upon the foundation of two-dimensional SAR imaging, utilizing multiple observations in the elevation direction to construct the synthetic aperture for the elevation imaging capability. The classic TomoSAR imaging algorithm typically refers to the third dimension as elevation, and research over the years has been based on this model. The process of three-dimensional imaging involves reconstructing the spatial distribution of scattering characteristics along the elevation direction. According to this model, for the same range-azimuth cell, discrimination of overlapping scatterers can be achieved based on different levels of sparsity. However, studies have found significant limitations of this model for urban buildings. Firstly, there is scatterer diffusion along the elevation direction, particularly at the junctions of building surfaces, such as the intersections between the facade and ground. Secondly, the maximum unambiguous range of the reconstruction model based on elevation is limited by the model. To address this issue, considering the characteristics of urban buildings, this paper proposes a novel imaging model based on the unique angle property. Specifically, the scatterer parameters to be estimated are transformed from the elevation coordinates to the off-nadir angle. Moreover, based on the non-penetrating property of electromagnetic waves for buildings, it is assumed that there is only one scatterer for each off-nadir angle, which is consistent with physical reality. Experiments were conducted based on measured data from two sites, and the results confirmed that the proposed model can effectively suppress clutter caused by multiple scattering, significantly improving the three-dimensional imaging quality.

LuTan-1 is the first spaceborne L-band bistatic Synthetic Aperture Radar(BiSAR) constellation in China aimed at highly accurate digital elevation model(DEM) and surface deformation monitoring. During the in-orbit commissioning phase, the constellation performed periodic observations over the Hami region in Xinjiang, China, while flying in a controlled helix formation. This paper preliminarily elucidates the capability and the stability of Raw DEM generation utilizing LuTan-1 BiSAR interferometric images. Interferometric processing procedures, including initial baseline estimation and absolute phase estimation, were implemented without the use of external ground control points(GCPs). In comparison with GCPs, the results showed that the mean error and the root mean square error (RMSE) of the eight Raw DEMs exhibit close alignment, accompanied by a small standard deviation, validating the excellent stability and capability of LuTan-1 Bistatic SAR.

Synthetic Aperture Radar Tomography (TomoSAR) has proven to be a powerful technique in urban mapping, disaster assessment, and counter-terrorism operations. However, existing inversion algorithms face challenges in balancing accuracy and efficiency. This paper introduces a novel gridless three-dimensional inversion method based on Weighted Least Squares (WLS) for TomoSAR, aiming to address issues related to accuracy, efficiency, and the lack of analytical expressions for the scatterer positions and backscattering coefficients. The validity of the proposed method is verified on the measured data.

Synthetic aperture radar (SAR) plays a crucial role in remote sensing because of its ability to operate in all weather conditions, both day and night. The traditional FFT-based SAR imaging algorithm suffers from severe speckle noise, which is almost inevitable owing to the coherent nature of the SAR system. Recently, the plug-and-play (PnP) SAR imaging method uses a plug-in denoiser as an image prior function to regularize the resulting image, thus suppressing speckle noise while maintaining the useful features of target objects. However, the existing plug-in denoisers used in statistical SAR imaging, either handcrafted or data-driven, are insufficient for complex remote sensing scenarios. More powerful image priors, such as the deep generative model for unconditional image generation, would be a better alternative regularizer for statistical SAR imaging. However, the most powerful diffusion generative model lacks an explicit latent space for conditional optimization to be adopted for SAR imaging from received signals. We propose a novel SAR imaging method based on conditional generation of a diffusion model. In detail, we embed the maximum a posteriori (MAP) formulation of SAR imaging from the received signal as a conditional guidance for diffusion generation, which overcomes the lack of latent space shortage. Compared with these statistical methods, our proposed methods exhibit exceedingly high performance both on simulated experiments and returned data imaging from RadarSat SAR data.

SAR tomography (TomoSAR) has the three-dimensional (3-D) resolving ability. Most existing 3D imaging methods for TomoSAR consider the layovers for different channels are the same. However, in the low-altitude airborne cases, the variation of layovers between channels becomes unneglectable. In this letter, we studied the channel migration phenomenon of sparse TomoSAR under low-altitude scenarios. We derived the model of the differential range from a particular target to different antennas, which is nearly a linear function along the array dimension. Therefore, we applied Keystone Transform on the array axis to correct this channel migration, and then the traditional compressed sensing-based 3D imaging methods for TomoSAR can be applied to get the final 3D reconstruction results. The approach was tested with simulation data and also the real data of the MV3DSAR system, which is a mini drone-borne TomoSAR system. The results demonstrate that the proposed method can provide a more accurate solution to the low-altitude sparse TomoSAR reconstruction problem.

There are shadows existing in traditional SAR tomography (TomoSAR) 3D imaging results, which bring difficulties in application of TomoSAR. However, TomoSAR with multi-azimuth observations can be applied to address this problem. In this paper, a panoramic 3D reconstruction method for TomoSAR based on multi-azimuth observations will be introduced. Firstly, 2D images are achieved with backprojection (BP) algorithm on the ground plane. Secondly, 3D reconstruction of TomoSAR is realized with orthogonal matching pursuit (OMP). After coordinate transformation, the panoramic 3D reconstruction results of TomoSAR based on multi-azimuth observations are achieved with registration of point clouds, in which iterative closest point algorithm (ICP) is effectively applied. Panoramic 3D reconstruction results of airborne TomoSAR experimental data can validate correctness and effectiveness of our method.

Three-dimensional imaging with tomographic SAR is a hot research topic in the field of SAR. The existing methods usually solve the question based on elevation discretization. When the target point has an off-grid deviation, the accuracy will decrease and the number of spurious points will increase. In this paper, a position-first step-by-step 3D inversion method is proposed. By constructing the observation equation with only an unknown elevation position, the height of the scattering center is directly solved in the continuous domain based on the least square criterion, and then the scattering coefficient is calculated based on the position. Simulation results verify the effectiveness of the method compared with the classical OMP algorithm. The proposed method has higher estimation accuracy under the same noise level and observation number and is less affected by the scattering center spacing.

Circular SAR (CSAR) is a typical 3-D imaging model of SAR. 3-D back-projection (BP) algorithm is a common time-domain 3-D imaging method for CSAR. However, 3-D imaging with back-projection algorithm has high algorithm complexity and low efficiency for processing pulse by pulse and grid by grid. This paper proposes a new fast 3-D imaging method for CSAR based on 3-D back-projection algorithm. In this method, 3-D interpolation and phase compensation operations can be transformed into 1-D interpolation and phase compensation operations and matrix searching operations with the construction of a geometric interpolation kernel. This proposed method can greatly improve imaging efficiency of CSAR when the error range allows. The simulated experimental results can prove the correctness and effectiveness of the proposed method.

Synthetic aperture radar (SAR) tomography (TomoSAR) systems have been widely used because of its capability of 3D reconstruction of urban area. However, due to the critical requirements of phase and amplitude accuracy, the channel imbalance of the airborne multi-baseline TomoSAR system must be estimated and compensated. In this paper, we proposed a channel imbalance calibration method with visual semantics. This method only uses the information from 2D SAR images and works well in the absence of ground control points (GCPs). Firstly, strong scatterer in 2D image is selected and the channel imbalances are estimated based on the SAR imaging geometry. Secondly, altitude of the scatterer is estimated with TomoSAR technique and the imaging geometry can be updated accordingly. After some iterations, the channel imbalance will converge and then be compensated. The estimated error is compared with the results based on GCPs and 3D point cloud are presented, which validates the feasibility of proposed method.