Enhanced Matrix Completion Method for Superresolution Tomography SAR Imaging: First Large-Scale Urban 3-D High-Resolution Results of LT-1 Satellites Using Monostatic Data

LuTan-1 (LT-1) constellation is an innovative distributed L-band spaceborne synthetic aperture radar (SAR), which can potentially provide tomographic mapping with twin satellites, i.e., tomographic SAR (TomoSAR) imaging. Compressed sensing (CS) techniques are often used in TomoSAR to improve elevation resolution by exploiting the layout sparsity. However, the off-grid effect of discrete dictionary used in traditional CS methods tends to degrade the imaging performance. In this paper, a novel non-convex enhanced matrix completion (NcEMC) algorithm is proposed for gridless super-resolution TomoSAR imaging. Specifically, a Hankel matrix completion model is designed to exploit the latent data structure in an off-grid manner. Benefiting from the enhanced low-rankness of the Hankel form, a refined uniform baseline observation is reconstructed from the original configuration via matrix completion optimization, achieving signal enhancement. To avoid using a regularization term to balance the traditional singular value decomposition solution of Hankel matrix, the proposed algorithm restates the low-rank constraint in a symmetric decomposition manner, which is beneficial to reduce the computation cost. Subsequently, an incoherence condition is introduced as a significant constraint in the Hankel matrix reconstruction process. To this end, a projected gradient descent iteration method is designed to satisfy the incoherence condition, thereby facilitating a more robust and accurate process of data reconstruction. Meanwhile, this paper presents the first large-scale urban 3-D high-resolution results of LT-1 satellites. We use 10 repeat-pass LT-1 monostatic SAR images to demonstrate the full processing workflow in TomoSAR imaging and the superiority of the proposed algorithm.