Precise RF-Vision Fusion UAV Positioning and Identification for 6G Spectrum Security

Precise positioning and identification of unauthorized unmanned aerial vehicles (UAVs) are of crucial importance for spectrum security and privacy protection in future intelligent networks. Although various single-modality approaches have been investigated, their performance degrades under the sensor-specific noise, resulting in suboptimal performance and robustness. To address these security challenges, we propose a multi-layer radio frequency (RF)-vision fusion framework that synergistically exploits temporal-spectral features of UAV RF signals and spatial-visual information to achieve precise and robust UAV positioning and identification. Moreover, a corresponding unified RF-Vision fusion Network (RFViNet) is designed to exploit the RF-vision cross-modal complementary and semantic synergy. Specifically, by leveraging the novel RFinformed proposal generation, RF-enhanced feature modulation, and RF-guided semantic query modules, the RFViNet effectively exploits the complementary strengths of RF and visual modalities. Furthermore, a practical RF–vision platform is developed to evaluate the performance of our method under various challenging conditions. Experimental results on the real-world dataset demonstrate that the proposed method achieves a competitive 85.8% average precision AP50, highlighting its potential for enhancing the spectrum security in future intelligent wireless networks.