High-Resolution Vortex Electromagnetic Wave Radar Sparse Imaging Using Efficient 2D-SLIM

Vortex electromagnetic wave (VEMW) radar, leveraging orbital angular momentum (OAM) modes for azimuthal information encoding, has emerged as a promising technology for high-resolution staring imaging. However, its performance is fundamentally constrained by the Bessel function modulation (BFM) effect and limited OAM mode availability, leading to azimuth-range coupling artifacts and energy inefficiency. Existing compressive sensing (CS) methods, particularly conventional sparse recovery techniques, struggle with sidelobe interference, noise sensitivity, and computational bottlenecks. To address these challenges, this paper proposes an efficient two-dimensional sparse learning via iterative minimization (2D-SLIM) framework, integrating physics-driven preprocessing with accelerated optimization. First, we establish a comprehensive VEMW radar signal model that explicitly incorporates BFM effects and OAM mode orthogonality. Second, building upon the beam steering strategy, we introduce an explicit analytical compensation factor to suppress the azimuth dependent energy attenuation caused by the squared Bessel term, thereby restoring the Fourier duality between azimuth angles and OAM mode indices. Third, we develop a dimensionally consistent 2D conjugate gradient least squares (2D-CGLS) algorithm, incorporating adaptive Barzilai-Borwein step sizes and non-convex group sparsity regularization. By leveraging Kronecker product factorization and matrix-form operations, the framework eliminates redundant vector-to-matrix conversions. Extensive numerical and EM simulations demonstrate that the proposed method achieves an improvement of more than 20% in image correlation value and a convergence rate more than 30% faster, while maintaining robust imaging performance under challenging conditions of limited OAM modes (e.g., | | 20 α ≤) and low SNR (down to -5 dB), outperforming several established CS techniques.