Single-Channel Super-Resolution SAR-GMTI via Atomic Norm Minimization

Synthetic aperture radar (SAR) imaging algorithms are designed to image stationary scatterers. A moving target is mis-positioned and results in artifacts in reconstructed images. In this paper, we present a novel approach to ground moving target imaging (GMTI) using a single channel SAR data based on atomic norm minimization (ANM) that is capable of localizing targets with sub-resolution cell precision. We use ANM to decompose the received SAR data into the echoes received from each scatterer in the scene, which facilitates processing echoes individually and isolating velocity related phase variations by cross-correlations across the slow-time samples. We use these estimates for motion compensation, and form the SAR image using the dual polynomial of the atomic decomposition. Motion parameters are then numerically estimated without a pre-specified grid. We derive the theoretical requirements on SAR imaging parameters, and detectable velocity limit; and demonstrate the off-grid, sub-resolution cell localization capability of our ANM-based method in SAR-GMTI setting by numerical simulations.