Sparse Stripmap SAR Autofocusing Imaging Combining Phase Error Estimation and L₁-Norm Regularization Reconstruction

Various reasons can induce phase error during synthetic aperture radar (SAR) data collection. If phase error is ignored during SAR imaging, the defocused image will be obtained. Although conventional autofocusing methods can eliminate phase error in the full-sampled case, they are not suitable for downsampled data. Sparse SAR imaging is a technique that combines sparse signal processing with SAR imaging. It can deal with both full- and downsampled data if the underlying scene admits a sparse representation in a particular domain. Autofocusing methods based on sparse SAR have been developed in the past decades. However, they are known to be developed for spotlight mode. Furthermore, there is a phase ambiguity problem in the existing methods, which will affect the uniqueness of the solution if not handled properly. In this article, we propose a sparse SAR autofocusing imaging method suitable for stripmap mode and give a solution to the phase ambiguity problem. The method jointly estimates phase error and reconstruct sparse SAR image in an iterative way. Each iteration consists of sparse imaging based on the phase-error- corrected echo and an update of phase error estimation. The experimental results based on the simulated and real data verify the effectiveness of the proposed method in coping with the full- or downsampled data corrupted by the phase error.