High-resolution sparse ISAR imaging based on frequency-selective atomic norm minimization

In practical inverse synthetic aperture radar (ISAR) imaging application, the data received by the radar is incomplete, which is called sparse aperture (SA) data. Sparse recovery (SR) theory can reconstruct unknown full aperture (FA) data from SA data by solving sparse optimization problems. The atomic norm minimization (ANM) solves the grid mismatch problem of the traditional SR theory effectively, but the imaging resolution is still limited. In this study, a sparse ISAR imaging method based on frequency-selective atomic norm minimization (FSANM) is proposed in this paper. The proposed method utilizes prior information to achieve frequency estimation within a given interval, effectively improving the resolution of the ANM method. The results of simulated and real data verify the effectiveness and superiority of the proposed method.