Sub-Nyquist SAR Imaging and Error Correction Via an Optimization-Based Algorithm

Area of Science:

• Remote Sensing
• Signal Processing
• Computational Imaging

Background:

• Sub-Nyquist sampling in Synthetic Aperture Radar (SAR) aims to widen swath coverage while maintaining azimuthal resolution.
• Existing optimization algorithms for sub-Nyquist SAR face challenges with parameter tuning and low signal-noise ratio (SNR) resistance.

Purpose of the Study:

• To propose a robust pseudo-L0-norm optimization algorithm for sub-Nyquist SAR systems.
• To enhance image reconstruction accuracy and overcome limitations of current methods.

Main Methods:

• Development of a modified regularization model incorporating scene prior information and Bayesian estimation.
• Application of the Cauchy-Newton method to solve the pseudo-L0-norm optimization problem.
• Integration of an error correction method to address motion-induced defocusing.

Main Results:

• The proposed pseudo-L0-norm optimization algorithm demonstrates improved SNR resistance compared to existing methods.
• Effective elimination of defocusing artifacts caused by motion-induced errors.
• Validation of the algorithm's effectiveness using simulated data and TerraSAR-X imagery.

Conclusions:

• The pseudo-L0-norm optimization algorithm offers a superior approach for sub-Nyquist SAR image reconstruction.
• The method addresses key challenges, leading to more accurate and reliable SAR imaging.