Detection and Tracking of a UAV Based on Low-Frequency Communication Network

When tracking a unmanned aerial vehicle (UAV) in complex backgrounds, environmental noise and clutter often obscure it. Traditional radar target tracking algorithms face multiple limitations when tracking a UAV, including high vulnerability to target occlusion and shape variations, as well as pronounced false alarms and missed detections in low signal-to-noise ratio (SNR) environments. To address these issues, this paper proposes a UAV detection and tracking algorithm based on a low-frequency communication network. The accuracy and effectiveness of the algorithm are validated through simulation experiments using field-measured point cloud data. Additionally, the key parameters of the algorithm are optimized through a process of selection and comparison, thereby improving the algorithm's precision. The experimental results show that the improved algorithm can significantly enhance the detection and tracking performance of the UAV under high clutter density conditions, effectively reduce the false alarm rate and markedly improve overall tracking performance metrics.