RF-Based Memory Augmentation for Cross-Modal Precise UAV Positioning

The rapid proliferation of UAV technology in civilian and military sectors has brought significant benefits but also raised concerns about unauthorized unmanned aerial vehicle (UAV), which pose potential risks to public safety. Current anti-UAV systems, which rely on radar, acoustic, antenna or visual sensors, encounter specific challenges, such as limited positioning ranges, susceptibility to noise, background interference and adverse environmental conditions. To address these limitations, we propose an RF-visual fusion-based memory augmentation network (RVUAV-Net) that integrates RF and visual images, enhancing the UAV positioning accuracy and robustness by utilizing the motion patterns derived from the historical UAV trajectory. Our proposed method capitalizes on the spatial-temporal characteristics of the historical data, enabling precise UAV positioning during partial occlusion and effective responses to high-speed movements. Moreover, our proposed RVUAV-Net minimizes false alarms in the complex environments by distinguishing UAVs from similar flying objects. Experimental results demonstrate superior positioning performance of our proposed method, highlighting its potential for anti-UAV in real-world scenarios where accuracy and continuity are critical.