An original 3-D high resolution radar imaging approach, called SPRITE for “SParse Radar Imaging TEchnique,” is presented. It incorporates in an original way the available prior knowledge about the electromagnetic backscattering, extending the commonly used sparse point-wise scatterers to sparse facet-wise scatterers. It is based on a regularization scheme that accounts for information of sparsity and support. The radar map formation is performed efficiently based on a penalized and constrained criterion, and an Alternating Direction Method of Multipliers algorithm, largely used in image restoration and machine learning. It is customized in a such way that, at each iteration, the map update is fast in the frequency domain by 3-D FFT and IFFT while the updates of the auxiliary variables are direct and separable. SPRITE is both evaluated on synthetic and real measurement data from a spherical measurement setup. In comparison to the conventional method, called Polar Format Algorithm, the resolution is drastically enhanced. The main scatterers are recovered with increased accuracy, leading to a deeper understanding of the scattering behavior. Furthermore, compared to recent $\ell _1$ based methods that are limited to point-wise scatterers, SPRITE and its facet-wise scatterers provide an impressive and improved spatial backscattering representation.

中文翻译：

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SPRITE：3D稀疏雷达成像技术

介绍了一种原始的 3-D 高分辨率雷达成像方法，称为 SPRITE，用于“稀疏雷达成像技术”。它以原始方式结合了有关电磁反向散射的可用先验知识，将常用的稀疏逐点散射体扩展到稀疏的逐面散射体。它基于一个正则化方案，该方案考虑了稀疏性和支持性信息。基于惩罚和约束标准以及乘法器交替方向方法算法有效地执行雷达地图形成，该算法主要用于图像恢复和机器学习。它的定制方式是，在每次迭代时，通过 3-D FFT 和 IFFT 在频域中快速更新地图，而辅助变量的更新是直接且可分离的。SPRITE 是根据来自球形测量装置的合成和真实测量数据进行评估的。与称为极坐标格式算法的传统方法相比，分辨率大大提高。主要散射体以更高的精度恢复，从而更深入地了解散射行为。此外，与最近仅限于逐点散射体的基于 $\ell_1$ 的方法相比，SPRITE 及其逐面散射体提供了令人印象深刻且改进的空间反向散射表示。