Two-dimensional gridless super-resolution method for ISAR imaging

TL;DR

This paper introduces a gridless super-resolution method called 2D-RWTM for improving ISAR imaging resolution by accurately recovering sparse scattering points without relying on grid fitting, outperforming existing methods like ANM.

Contribution

The paper presents a novel 2D-Reweighted Trace Minimization technique that enhances ISAR image resolution by effectively recovering sparse scattering points in both range and cross-range directions.

Findings

2D-RWTM successfully recovers scattering points in simulations.

The method outperforms Atomic Norm Minimization in terms of MSE.

Recovery remains effective with different weighting matrices and adjacent points.

Abstract

We are focused on improving the resolution of images of moving targets in Inverse Synthetic Aperture Radar (ISAR) imaging. This could be achieved by recovering the scattering points of a target that have stronger reflections than other target points, leading to increasing the higher Radar Cross Section (RCS) of a target. These points, however, are sparse and when the received data is incomplete, moving targets would not be properly recognizable in ISAR images. To increase the resolution in ISAR imaging, we propose the 2-Dimensional Reweighted Trace Minimization (2D-RWTM) method to retrieve frequencies of sparse scattering points in both range and cross-range directions. This method is a gridless super-resolution method, which does not depend on fitting the scattering point on the grids, leading to less complexity compared to the other methods. Using computer simulations, the proposed 2D-RWTM is compared to the Atomic Norm Minimization (ANM) in terms of the Mean Squared Errors (MSE). The results show that using the proposed method, the scattering points of a target are successfully recovered. It is shown that by selecting different weighting matrices and scattering points adjacent to each other, the recovery in ISAR imaging is still successful.