Sparse SAR Separation Imaging via Exact Observation: Initial Result

In synthetic aperture radar (SAR) imaging, modulation of echo by target velocity necessitates more complex approaches for imaging moving targets. Concurrently, the scenes are generally mixed with moving and stationary targets. Achieving simultaneously separated imaging of moving and stationary targets represents a challenge in SAR imaging. In this letter, we proposed a novel separable iterative reweighted algorithm (SIRA), which can achieve separated imaging of moving and stationary targets. First, moving and stationary targets are both considered in the imaging model. Then, by separately applying sparse constraints to stationary and moving targets, the regularization problem is constructed to address the imaging of both moving and stationary targets. Finally, stationary targets are extracted by solving the Karush-Kuhn-Tucker (KKT) conditions of the problem, and moving targets are obtained through algorithm iteration. Experiments based on simulated data validate the effectiveness of the proposed method.