Two-Dimension Joint Super-Resolution ISAR Imaging With Joint Motion Compensation and Azimuth Scaling

The quality of inverse synthetic aperture radar (ISAR) images suffers seriously from the two-dimension (2-D) resolution and noise. The motion errors arising from translational and rotational motion further aggravate the image defocusing. For the limited bandwidth and short aperture (LB-SA) signal, this letter proposes a novel 2-D joint super-resolution (2D-JSR) ISAR imaging with joint motion compensation and azimuth scaling (JMCAS) algorithm. In this technique, a 2D-JSR signal model is established, enabling the 2-D high-resolution ISAR image to be generated by solving a sparsity-driven optimization problem with a modified quasi-Newton solver. In addition, a new JMCAS algorithm is developed to enhance the focusing performance of image. Not only can this algorithm jointly correct the range shift and phase error caused by translational motion, it can also complete the azimuth scaling and range spatial-variant phase error (RSVPE) compensation simultaneously. Through the iterative processing of 2D-JSR reconstruction and JMCAS, the well-focused and scaled high-resolution ISAR image can be obtained. Both simulated and real data experiments are provided to verify the effectiveness of the proposed algorithm.

Automated summary

This paper introduces a novel 2-D joint super-resolution (2D-JSR) ISAR imaging method, which improves the resolution and focusing performance of images through joint motion compensation and azimuth scaling. By solving a sparsity-driven optimization problem with a modified quasi-Newton solver, high-resolution images are generated, while a new JMCAS algorithm enhances focusing and scaling effects.