Sub-Nyquist SAR Imaging And Error Correction Via Optimization-based Algorithm

Abstract

Sub-Nyquist synthetic aperture radar (SAR) based on pseudo-random time-space modulation has been proposed to increase swath width while preserving the azimuthal resolution. Due to the sub-Nyquist sampling, the scene can be recovered by the optimization-based algorithm. However, these methods suffer from some issues, e.g., manually tuning difficulty and pre-definition of optimization parameters, and low signal-noise-ratio (SNR) resistance. To address these issues, a reweighted optimization algorithm named pseudo-Λ0-norm optimization algorithm is proposed for the sub-Nyquist SAR system in this paper. A modified regularization model is first built by applying the scene prior information to nearly acquire the number of non-zero elements based on Bayesian estimation, and then this model is solved by the Cauchy-Newton method. Additionally, an error correction method combined with our proposed pseudo-Λ0-norm optimization algorithm is also present to eliminate defocusing for the motion-induced model. Finally, experiments with simulated signals and strip-map TerraSAR-X images are carried out to demonstrate the effectiveness and superiority of our proposed algorithm.