Elevating ISAR Imaging Resolution via 2-D SLIM

The emergence of compressed sensing (CS) techniques has provided us with the possibility of creating high-resolution inverse synthetic aperture radar (ISAR) images for moving targets, even with limited measurements. A recent approach called sparse learning via iterative minimization (SLIM) is proposed as a technique for the recovery of 1-D sparse signals, which exhibits high computational complexity and memory requirements when applied to the ISAR imaging problem. In this article, we present a novel 2-D implementation of the latter technique called 2D-SLIM which substantially improves on the computational complexity and memory requirement of the conventional SLIM for the particular case of ISAR image recovery. In addition, our numerical results on synthetic and real data confirm the superior performance of 2D-SLIM under noisy settings compared to alternatives such as 2-D fast Fourier transform (2D-FFT), 2-D gradient projection sequential order one negative exponential (2D-GP-SOONE), and 2-D smoothed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula> pseudo-norm (2D-SL0).