Point Target Imaging Using CS Algorithm for SAR Imaging

This MATLAB simulation program implements point target imaging using the Compressed Sensing (CS) algorithm for Synthetic Aperture Radar (SAR) imaging. SAR imaging is a technique that acquires target images through synthetic aperture radar systems, where the CS algorithm serves as a compressed sensing method for processing SAR imagery. Point target imaging refers to achieving clear and visible imaging of point targets within SAR images. The MATLAB simulation program works by modeling SAR imaging scenarios and generating corresponding images through computational methods. The implementation typically involves key steps such as radar signal simulation, measurement matrix design using random sampling patterns, and reconstruction algorithms like L1-norm minimization through optimization techniques such as Basis Pursuit or Orthogonal Matching Pursuit. Core functions may include radar parameter configuration, echo signal generation, sparse transformation operations, and reconstruction quality evaluation metrics. By developing this SAR imaging CS algorithm simulation for point targets, researchers can better understand and analyze SAR imaging techniques, particularly in studying sparse signal recovery performance under various sampling conditions and noise environments.