Iterative Optimization-Based ISAR Imaging With Sparse Aperture and Its Application in Interferometric ISAR Imaging

Recently, high-resolution inverse synthetic aperture radar (ISAR) imaging with sparse aperture (SA) data has attracted increasing attention. The theory of compressive sensing (CS) suggests that an unknown sparse signal can be accurately recovered by taking advantage of very limited samples. For ISAR images, the number of the resolution cells occupied by the strong scattering points of the target is usually much smaller than that of the resolution cells of the image plane, revealing the strong sparsity trait of the ISAR signal. This trait of ISAR signal creates the conditions for incorporating CS into high-resolution ISAR imaging. In this paper, a novel iterative optimization-based SA-ISAR imaging approach is proposed. First, the SA-ISAR signal model is established and the envelope alignment is executed on the one-dimensional range profiles of the SA data. Next, the gradient-based algorithm is exploited to recover the complete signals. Then, by iteratively performing the procedures of the envelope alignment and the signal recovery, the accuracy of signal recovery can be significantly improved and a high-quality ISAR image can be obtained. Ultimately, the extension of the iterative optimization-based SA-ISAR imaging to the three-dimensional (3-D) interferometric ISAR (InISAR) imaging is successfully implemented via the traditional ISAR imagery pair interferometric method. The experiments based on the measured and simulated data are carried out to validate the superiority of the novel algorithm.