Super-resolution SAR tomography focusing by lp — Norm regularization-the FOCUSS algorithm

The Compressive Sensing (CS) theory, which was first promulgated by Candes and Donoho in 2004, can break through the Nyquist Sampling Theorem to solve the bottleneck problems on practice such as the high-speed sampling and the mass-data storing. In this paper, the CS idea is applied to multi-baseline SAR tomography 3-D imaging. Aiming at solving the bad imaging quality problem caused by sparse baselines in practical applications, the sparse model in the height direction of multi-baseline SAR is built according to the limited scattering centers hypothesis. Thus the problem of imaging in the height direction is transformed to the problem of reconstructing a sparse signal. And the l_p-norm minimization methods are utilized to solve this problem. Simulation results show that the proposed method not only can overcome the limitation of Rayleigh Criterion, but also can solve the problem brought about by the non-uniform distribution of baselines in noisy environment.