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Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

Conventional radar cross section (RCS) measurements require far-field or compact-antennatest-range (CATR) conditions, which have strict restrictions and high implementation costs. By contrast, RCS extrapolation methods in the near zone are more convenient and practical, which become the research emphasis in recent years. This paper presents a novel RCS extrapolation method based on the combination of near-field 3D synthetic aperture radar (SAR) technique and planar projective transforms (PPT) algorithm. Firstly, to extract the target's reflectivity distribution, we apply near-field 3D SAR imaging and obtain 3D near-field RCS (NFRCS) images. Secondly, to meet the requirement of RCS measurements, we derive a novel correction factor used to precisely expand 3D NFRCS images. Finally, we extrapolate the plane-wave responses in the azimuth-vertical dimensions, which presents as planar patterns. The proposed method utilizes the complete 3D image-based information to overcome the inherent constraints of classical RCS extrapolation methods on application scenarios, which has the advantages of broad applicability and high flexibility. The detailed derivation and implementation of this method are described in this paper. The simulation and experiment results verify that the proposed method can provide the equivalent CATR result within ±11 • azimuth and elevation angles, and it is applicable to precisely measuring the point, surface, and complex scatterers. INDEX TERMS Radar cross section, synthetic aperture radar, near-field 3D imaging, far-field extrapolation, planar projective transforms.

The environmental interference and the noise in 3D synthetic aperture radar (SAR) image, considered as the background, are inescapable and ought to be eliminated. For 3D SAR image, there is a spatial separation of the target and the background. Therefore, it is possible to achieve the separation of the target and the background by image segmentation. Due to the complexity of the target shape and the large dynamic range of the SAR image, the background cannot be accurately separated through the amplitude information alone. In this paper, a method based on region growing is proposed to achieve 3D SAR image background separation utilizing the plural and the spatial information. The image enhancement matrix, constructed by the plural information of the SAR image, is implemented to improve the contrast of the image. The seeds are extracted by the weighted Otsu, and the weight is determined by the structure and amplitude information from the target. For the region growing, the growing process is achieved by the accumulation of the growing rate, which can suppress the growing of the noise. During the region growing, the stopping growing condition of each seed is independent and controlled by the seed threshold. The global threshold constrains the almost unrestricted growing of the seed whose amplitude is close to the noise amplitude. The results of the simulation and the experiments verify the performance of the proposed method is higher than that of the compared methods with three image evaluation criteria. Besides, we discuss the cost of computation and the influences of three important parameters to achieve a complete analysis.INDEX TERMS Synthetic aperture radar (SAR), image segmentation, region growing, image denoising.

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