Two-dimensional gridless super-resolution method for ISAR imaging

We are focused on improving the resolution of images of moving targets in inverse synthetic aperture radar (ISAR) imaging. This can be achieved by recovering the scattering points of a target that have stronger reflections than other target points, resulting in a higher radar cross section of a target. However, these points are sparse and moving targets cannot be correctly detected in ISAR images. To increase the resolution in ISAR imaging, we propose the fast reweighted trace minimization (FRWTM) method to retrieve frequencies of sparse scattering points in both range and azimuth directions. This method is a two-dimensional gridless super-resolution method that does not depend on fitting the scattering point on the grids. Using computer simulations, the proposed algorithm is compared with fast reweighted atomic norm minimization (FRANM), sparse Bayesian learning (SBL), and SL0 algorithms in terms of mean squared error (MSE). The results show that FRWTM performs better than the other methods, especially SBL and SL0 at low signal-to-noise ratio (SNR) and fewer samples.