Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples

Sparse imaging is widely used in synthetic aperture radar (SAR) imaging. Compared with the traditional matched filtering (MF) methods, sparse SAR can directly image scattered points of a target and effectively reduce sidelobes clutter irregular samples. However, view large-scale computational complexity reconstruction raw echo data, algorithms often require huge expense. To solve above problems, this paper, we propose 3D near-field direct algorithm for trajectories, adopting piece preliminary information to update dictionary matrix dimension, using Gaussian iterative method, optimizing signal-processing techniques, which achieve more rapid manner. The proposed was validated through simulations empirical study scanning scenarios compared MF shown significantly computation time preserve complex scenes high-resolution reconstruction.