With the development of airborne synthetic aperture radar (SAR) technology, the 3D SAR point cloud reconstruction has emerged as a crucial development trend in the current SAR community. However, due to measurement errors, environmental interference, radar decoherence, and other noises associated with the SAR system, the reconstructed tomogram is often deteriorated by numerous noisy scatterers. As a result, it becomes challenging to obtain high-quality 3D point clouds of the observed object, making it difficult to further process the point cloud and realize target identification. To address these issues, we propose a K nearest neighbor comprehensive weighted filtering algorithm. The filtered point cloud is evaluated quantitatively using three-dimensional entropy. In this study, we adopted various filtering methods for simulated data, P-band data of Genhe, and Ku-band data of Yuncheng to refine the tomogram and compare their performances. Both qualitative and quantitative analyses demonstrate the superiority of the filtering algorithm proposed in this paper.

The classical planar-wavefront-based TomoSAR imaging model suffers from the problem that the effective integration interval is not enough to cover the target distribution region in the low-altitude airborne case. It will lead to a deterioration of the performance of tomogram reconstruction and inaccuracy of estimated scatterers. This paper reviews the exact and approximate forms of the aforementioned inaccurate model based on planar wavefront and points out the problem with the conventional model. To solve this problem, we propose spherical wavefront models with the exact form or an approximate form of the slant range formula. The estimated variable for the scatterer’s location is converted from elevation to off-nadir angle, and the effective integration interval has been extended. In addition, we explore relationships between the exact form of the conventional model and the exact form of the proposed model, and the relationship between the approximate form of the conventional model and the approximate form of the proposed model. This provides a basis for modifying the inversion algorithm that is designed based on the conventional model to adapt to the low-altitude airborne case. Eventually, through experiments based on simulated data and measured data, the imprecise reconstructions obtained with the conventional model are demonstrated, and the correctness of spherical wavefront models and the effectiveness of transformation between models are proved.

Tomographic synthetic aperture radar (TomoSAR) has the ability to separate mixed scatterers, making it highly suitable for urban 3-dimensional (3D) reconstruction. However, Urban TomoSAR imaging still faces challenges such as resolution limitations, multipath effects, the uncertainty on the flight track, and registration errors, resulting in sparse point clouds with holes and low accuracy. In this paper, we propose a Geometric Semantic Enhanced TomoSAR Reconstruction Algorithm (Geo-SETRA) for urban area. Geo-SETRA integrates geometric structures, extracted from TomoSAR point clouds, as prior distributions for elevation estimation using Bayesian methods. We first construct a sparse optimization model based on both compressed sensing and maximum a posteriori estimation, and also give its solution. Further, the Cramér–Rao lower bound of this algorithm is derived to theoretically illustrate how it improves imaging accuracy. Both simulated data and real-data experiments prove that our method is feasible and effective in urban 3D reconstruction. As a result, our method successfully produced a dense and realistic 3D scattering model for urban areas with minimal postprocessing, preserving detailed geometric structures and retaining over 80% of the points in the final model.

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 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.