Joint Spatial-Polarimetric Sparse-Reconstruction Beamforming for Interference Mitigation in Dual-Polarized Arrays

This paper presents a novel approach to beamforming using joint spatial and polarization domains through a polarimetric sparse-reconstruction (PSR) framework. The proposed method leverages dictionary-based sparse representation techniques specifically optimized for single interference scenarios in dual-polarized antenna arrays. Our framework employs an adaptive dictionary refinement strategy that focuses computational resources on regions of interest in the parameter space, enabling effective discrimination between signals with similar spatial characteristics but different polarization states. Extensive simulations demonstrate that our approach significantly outperforms conventional Minimum Variance Distortion-less Response (MVDR) beamforming, achieving a remarkable Signal-to-Interference-plus-Noise Ratio (SINR) improvement of 38.87 dB in challenging scenarios where interference and signal share similar spatial locations but differ in polarization characteristics. The paper provides a comprehensive mathematical formulation, detailed algorithmic implementation, and thorough performance analysis through beam pattern visualizations and dictionary evolution tracking. The results offer important insights for practical applications in modern wireless communications, radar systems, and polarimetric sensing, particularly in dense environments where conventional spatial-only beamforming techniques face significant limitations.