A Fast ISAR Imaging Method Based on PC-2D-FIR-GEM-Net for Low SNR and Sparse Aperture Conditions

High-resolution inverse synthetic aperture radar (ISAR) imaging under low signal-to-noise ratio (SNR) and sparse-aperture conditions remains challenging due to severe sidelobe artifacts, weak-scatterer loss, and high computational burden. Although sparse Bayesian learning (SBL) methods are robust to noise, most existing formulations assign pixel-wise independent hyperparameters to image coefficients, which limits their ability to characterize the spatial clustering of scattering centers. Moreover, conventional Bayesian inference often involves large-scale matrix inversion and iterative optimization, leading to high computational cost. To address these issues, this paper proposes a fast ISAR imaging method termed pattern-coupled (PC) two-dimensional (2D) fast inverse-free reconstruction (FIR) generalized expectation-maximization (GEM) network (PC-2D-FIR-GEM-Net), which integrates pattern-coupled hierarchical Bayesian modeling, inverse-free generalized expectation-maximization (GEM) inference, and model-driven deep unfolding. A pattern-coupled prior is first introduced to exploit local structural dependencies among neighboring scatterers, which improves the recovery of weak and clustered scattering structures. Then, an inverse-free GEM solver is developed by constructing surrogate objectives so that image updating can be performed without explicit matrix inversion. Finally, the iterative solver is unfolded into a finite-stage network, where a lightweight convolutional neural network (CNN) learns the coupled precision field and stage-wise update parameters while preserving the model-driven inverse-free update structure. Experimental results on both simulated and measured ISAR datasets demonstrate that the proposed method achieves improved focusing quality, better structural preservation, and significantly reduced computational time under challenging sparse-aperture and low-SNR conditions.