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.

With increasing demands from both military and civilian applications, ground moving-target imaging is becoming one of the important research topics for high-resolution SAR systems. However, the existing moving-target imaging methods are not suitable for high-resolution SAR because of their low parameter estimation accuracy and high computational complexity. To solve the problem, an improved ground moving-target parameter estimation and imaging method is proposed. First, the third-order phase model of the uniformly accelerated target signal is constructed, and the Hough transform and the second-order Keystone transform (SOKT) are used to correct the range cell migration into one range cell to achieve target coherent accumulation. Secondly, a delayed cross-correlation function (DCCF) is constructed to reduce the order of the range migration phase response in the slow time domain, and the coupling degree between the cross-correlation peak position and the range migration is reduced, so that the obtained DCCF has a higher gain, which ensures the accuracy of parameter estimation. Parameter estimation is simplified to peak detection by the Shift-And-Correlation (SAC) algorithm and two-dimensional Fourier transform (2D-FFT), avoiding parameter search. Compared with the existing methods, the proposed method has better focusing effect and lower computational complexity. Finally, simulation and measured data are given to verify the effectiveness of the proposed method.