Sidelobe Suppression for High-Resolution Terahertz ISAR Imaging of Space Targets Based on ADMM

Terahertz (THz) inverse synthetic aperture radar (ISAR) demonstrates unique advantages in space target imaging, enabling submillimeter-level high-resolution imaging and providing crucial technical support for all-weather space situational awareness (SSA). However, conventional fast Fourier transform (FFT)-based imaging methods exhibit significant limitations in highly dynamic scenarios, where imaging quality deteriorates substantially. To address these challenges, this study innovatively proposes a compressed sensing (CS) imaging framework based on the adaptive alternating direction method of multipliers (ADMM). The framework features several key technical innovations: First, to address the masking effect of strong scattering points in space targets, a novel sidelobe suppression constraint mechanism is designed to effectively suppress noise and sidelobe interference while preserving weak scattering features. Second, dynamic adjustment of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> regularization weight significantly mitigates image distortion caused by traditional optimization with fixed regularization parameters. Notably, for the sensitivity issue of regularization parameters across different imaging scenarios, an adaptive parameter optimization strategy based on line search is proposed, providing theoretical guarantees for high-precision imaging. At the implementation level, this solution innovatively transforms the ISAR imaging problem into a two-dimensional matrix optimization model, substantially reducing memory requirements while significantly improving computational efficiency. Extensive simulations and experimental measurements demonstrate that compared to state-of-the-art methods, the proposed approach achieves approximately <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-16.2\,$</tex-math></inline-formula>dB and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$-15.42\,$</tex-math></inline-formula>dB improvement in peak sidelobe ratio (PSLR) and integrated sidelobe ratio (ISLR) in both range and azimuth directions, respectively, along with over 9.21% reduction in image entropy. These performance advantages lead to 11<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvements in target component detection confidence in complex environments, providing important technical references for next-generation space surveillance radar systems.