Super-resolution Imaging for Vortex Electromagnetic Wave Radar Based on Mode Correlation Weighting and Adaptive Regularization

Vortex Electromagnetic Wave Radar (VEWR) leverages the orthogonality of Orbital Angular Momentum (OAM) modes, introducing a new physical dimension that theoretically overcomes the azimuth resolution limitations of conventional radar systems and enables enhanced micro-motion perception and forward-looking imaging. However, in practical engineering applications, the limited number of available OAM modes and the presence of complex electromagnetic noise often cause severe mode aliasing and resolution degradation. Existing sparse imaging methods face inherent trade-offs between accuracy and computational efficiency and exhibit limited robustness to noise. To address these issues, this paper proposes a super-resolution imaging framework that integrates mode correlation weighting and adaptive regularization. First, a forward-looking imaging geometry and a wavefront-modulated signal model for VEWR are established. Subsequently, an OAM mode correlation matrix is designed to characterize the nonuniform distribution of radiation energy among modes, where Bessel-function-modulated weights reinforce the low-rank constraints of dominant radiation components. Finally, a compound optimization model combining sparsity and low-rankness priors is developed, incorporating an adaptive weighting mechanism that dynamically balances structural preservation and noise suppression. A joint optimization framework based on the alternating direction method of multipliers and augmented Lagrange multiplier algorithms is constructed, in which the core image-updating subproblem is efficiently solved using a momentum-accelerated two-dimensional conjugate gradient least-squares method. Numerical simulations and electromagnetic experiments verify that the proposed method preserves target structural integrity under limited modes and strong noise, while effectively improving both imaging quality and computational efficiency.