Downward-Looking Linear Array Three-Dimensional SAR Imaging Based on the Two-Dimensional Mismatch Compensation

For downward-looking linear array (DLLA) three-dimensional (3-D) synthetic aperture radar (SAR), it is necessary to realize the super-resolution in both azimuth and cross-track direction due limited lengths of array. As all scatterers are assumed on uniform grids, can be achieved by 1-D compressed sensing. In real imaging system, however, gridding error should considered because biased lead mismatch measurement matrix affect performance. The has been solved atomic norm minimization off-grid sparse Bayesian inference. With development methods, 2-D realized sensing (CS) algorithms. To solve problem, a novel compensation method for DLLA 3-D SAR proposed. Instead converting signals vectors, proposed directly processes with joint model. Furthermore, model simplified as normal model, which suitable most CS reconstruction It not only provide better performance but also reduce memory cost computation load. Finally, simulation experiments shown demonstrate validity method.