Near-Field Radar Imaging via Compressive Sensing

Abstract

A novel two-dimensional (2-D) compressive sensing (CS) based method is presented for near-field radar imaging. First, an accurate near-field approximation is proposed, based on which the circular wavefront curvature of spherical waves can be compensated by mapping the images to a rectified new grid. More importantly, the near-field approximation makes the two dimensions of the scattered data separable for the range and cross-range directions, which makes it possible to solve the 2-D reflectivity matrix for the image reconstruction directly. Then, a 2-D proximal subgradient algorithm for near-field radar imaging based on a fast iterative shrinkage/thresholding algorithm (FISTA) is introduced to resolve the memory usage and computation time issues. Simulation and experimental results are provided to demonstrate the performance of the proposed method with comparisons to the traditional Fourier-based method and to the conjugate gradient (CG) based method, which proves that the proposed method is an effective way to solve the near-field radar imaging problem.