Sparse Bayesian SAR imaging of moving target via the EXCOV method

This paper presents a method for imaging of moving targets via the compress sensing by treating the imaging as a problem of signal representation in an over-complete dictionary. The essential idea behind sparse signal representation models comes from the fact that SAR ground moving targets are sparsely distributed in the observation scene and the received SAR echo is decomposed into the sum of basis sub-signals, which are generated by discretizing the target spatial domain and velocity domain. A sparse Bayesian recovering method named the expansion-compression variance-component based method (ExCoV) is used for image reconstruction since it is automatic and demands no prior knowledge about signal-sparsity or measure-noise levels, which is significantly faster than sparse Bayesian learning, particularly in large-scale problems. The numerical experiments using ExCoV method have estimated moving-targets at different velocities in the case of low SNR, and the target image has higher resolution and lower side-lobe as the number of measurements is small compared with traditional algorithms.