Compressed sensing Linear array SAR 3-D imaging via sparse locations prediction

Abstract

The 3-D image of Linear array synthetic aperture radar (LASAR) usually exhibit high sparseness, so sparse imaging algorithms based on compressed sensing (CS) theory can be used for LASAR 3-D imaging. However, the conventional CS-based imaging scheme suffers from huge computational time, especially for large scene imaging, which requires a huge sensing matrix to reconstruct the whole scene. In this paper, a sparse locations prediction strategy is proposed for CS-based LASAR 3-D imaging. The sparse target cells of the scene is firstly estimated by location prediction method with the traditional image, and then the scene is split into subspaces and the echo data is segmented into subsets, the measurement matrix is constructed only using the sparse cells of the subspaces, so that the reconstruction time can be reduced significantly. Simulation and experiment results demonstrates the validity of the approach.