Correction of Range-Variant Motion Error and Residual RCM in Sparse Regularization SAR Imaging

Area of Science:

• Remote Sensing
• Signal Processing
• Geophysics

Background:

• Sparse Synthetic Aperture Radar (SAR) imaging benefits from Lq regularization.
• Motion errors in SAR systems cause image degradation and defocus, particularly range-variant errors in high-resolution systems.
• Residual range cell migration (RCM) significantly impacts image quality.

Purpose of the Study:

• To propose a novel azimuth-range decoupled sparse SAR imaging method.
• To address range-variant motion errors and residual RCM.
• To enhance the accuracy and quality of SAR image reconstruction.

Main Methods:

• A one-step motion compensation (MOCO) operator using inertial navigation systems (INS)/global positioning systems (GPS) data.
• A coarse-to-fine approach for range-variant motion error correction.
• A joint imaging and phase-error estimation scheme for fine high-order phase correction.

Main Results:

• The proposed MOCO operator effectively reduces residual RCM.
• The method significantly improves reconstruction accuracy and image focusing quality.
• Experimental results validate the effectiveness of the proposed sparse SAR imaging technique.

Conclusions:

• The novel azimuth-range decoupled method successfully corrects range-variant motion errors and residual RCM in sparse SAR imaging.
• The integrated approach enhances SAR image quality and focusing.
• The method offers a robust solution for high-resolution SAR systems affected by motion inaccuracies.