Gridless high-resolution sparse ISAR imaging method based on atomic norm minimization with Hankel-Toeplitz model

Abstract

In practical applications of inverse synthetic aperture radar (ISAR), continuous long-time observation is not allowed due to transmission errors or noise interference. An effective way to reconstruct missing data would be to apply the sparse recovery (SR) method. However, the traditional SR methods need to discretization the parameter space, which inevitably leads to grid mismatch. The atomic norm minimization (ANM) method based on a continuous parameter estimation model can effectively eliminate the impact of grid mismatch and achieve high-resolution ISAR imaging. In this study, a sparse ISAR imaging method based on atomic norm minimization with Hankel-Toeplitz (ANM-HT) model was proposed to obtain better imaging performance. By reformulating the ANM-HT as semi-definite programming (SDP), the complete echo data can be recovered using SDP3 solvers. Real data results demonstrate the effectiveness and superiority of the proposed method.