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.

Uncrewed aerial vehicles (UAVs) positioning is of crucial importance in diverse applications. However, it is extremely challenging to realize the precise UAVs positioning over long distances due to the small size and dramatic scale variations associated with the high mobility in the wide area. To tackle this issue, a multimodal scale normalization framework is proposed for the scale-robust precise pixel-level UAV positioning. The framework exploits our proposed distance-aware image slicing and distance-aware scale normalization module. Moreover, a modal fusion-based scale normalization network is proposed that can accept arbitrary low-resolution UAV patches and produce the consistent high-resolution images at a uniform UAV instance scale with a single learnable model. The proposed framework is generic and can be directly used in the existing pixel-level positioning pipelines to improve the positioning performance and scale robustness. To verify the proposed framework in the real application, a practical vision-radar UAV positioning system is developed. Experimental results on the real-world dataset demonstrate the generality and effectiveness of our framework. Moreover, the ablation experiments also confirm the contribution of each module in the framework.

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.