Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) can be used to directly obtain the three-dimensional (3D) imagery of the illuminated scene with a single track. Due to the length limitations of synthetic aperture and antenna array, the super-resolution algorithms within the framework of 2D compressive sensing (CS) have been conceived to reconstruct the azimuth-cross-track plane image because of its spatial sparsity. Since the desired scatterers are presupposed to be distributed over a series of fixed grid points, the location accuracy of the existing 2D CS algorithms is relatively low. To overcome this problem, a fast 2D gridless recovery (GLR) algorithm for the 2D imaging signal model established in the real domain is proposed in this study. First, two different forms of 2D real-valued signal models with uniform or random sampling on the azimuth-cross-track plane are reconstructed by means of unitary transformation. Further, the real-domain based 2D sparse signal gridless reconstruction approach is derived. Finally, extensive simulation results validate that the proposed 2D real-valued GLR approach can approximately improve the computational efficiency by a factor of ten in terms of CPU time when compared with that of the 2D GLR algorithm in the complex domain.

中文翻译：

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用于MIMO阵列SAR 3D成像的快速二维稀疏信号无网格恢复算法

多输入多输出（MIMO）阵列合成孔径雷达（SAR）可用于直接获得具有单个轨迹的照明场景的三维（3D）图像。由于合成孔径和天线阵列的长度限制，由于其空间稀疏性，已经构想了二维压缩感测（CS）框架内的超分辨率算法可重建方位角交叉轨迹平面图像。由于假定期望的散射体分布在一系列固定的网格点上，因此现有2D CS算法的定位精度相对较低。为了克服这个问题，本研究提出了一种针对实域中建立的二维成像信号模型的快速二维无网格恢复（GLR）算法。第一，借助unit变换，重构了两种不同形式的在方位角交叉轨迹平面上进行均匀或随机采样的二维实值信号模型。此外，推导了基于实域的2D稀疏信号无网格重构方法。最后，大量的仿真结果证明，与复杂域中的2D GLR算法相比，所提出的2D实值GLR方法在CPU时间方面可以将计算效率大约提高十倍。