Measurement Matrix Optimization and Mismatch Problem Compensation for DLSLA 3-D SAR Cross-Track Reconstruction

Area of Science:

• Radar Systems Engineering
• Signal Processing
• Computational Imaging

Background:

• Downward looking sparse linear array 3-D SAR (DLSLA 3-D SAR) enables 3-D imaging.
• Improving cross-track resolution is crucial for DLSLA 3-D SAR.
• Compressive sensing (CS) reconstruction performance relies on the measurement matrix.

Purpose of the Study:

• Optimize the measurement matrix for DLSLA 3-D SAR.
• Address measurement matrix mismatch caused by off-grid scatterers.
• Enhance cross-track resolution in 3-D SAR imaging.

Main Methods:

• Proposed two mutual coherence-based criteria to optimize antenna phase center (APC) configuration.
• Introduced sparse Bayesian inference for joint estimation of scatterers and off-grid error.
• Applied CS framework for cross-track reconstruction.

Main Results:

• Demonstrated improved performance of proposed APC configuration schemes.
• Showcased effectiveness of sparse Bayesian inference in compensating for measurement matrix mismatch.
• Achieved enhanced cross-track resolution in 3-D SAR images.

Conclusions:

• The proposed methods effectively optimize the measurement matrix for DLSLA 3-D SAR.
• Sparse Bayesian inference successfully handles off-grid scatterer issues.
• The study contributes to advancing high-resolution 3-D SAR imaging techniques.