An RF-Visual Directional Fusion Framework for Precise UAV Positioning

Anti-unmanned aerial vehicle (UAV) systems are crucial for preventing unauthorized individuals from exploiting UAVs for illegal activities, including surveillance and attacks. Precise real-time positioning of small UAVs is the premise of the effective operation of anti-UAV systems. However, its performance is confined due to the small size of the target and its high susceptibility to disturbance caused by birds or other aircraft. To tackle this problem, a radio-frequency (RF)-visual directional fusion framework is proposed for precise UAV positioning. In the framework, radio signals are aligned with images by jointly calibrating the array antenna and camera. The spatial spectrum is extracted by an array antenna to concentrate on target areas within the image modal. Moreover, in order to improve the precision of joint calibration, a segmentation-based denoising method is proposed to remove the spectrum noise. Furthermore, a practical anti-UAV positioning platform is established, and two synchronized data sets, which include visual images and UAV RF signals, are collected on the platform. Experimental results demonstrate that our proposed framework improves positioning accuracy and robustness compared to the benchmark methods.