RaLMEN: A Robust UAV Recognition Framework for Low-Altitude Traffic Surveillance

With the rapid development of the low-altitude economy, Unmanned Aerial Vehicle (UAV) recognition has become increasingly important. However, vision-based methods progressively become ineffective as the UAV's flight altitude increases. Therefore, radar information has emerged as the predominant approach for UAV recognition. However, existing low-altitude targets recognition methods either depend on long-sequence trajectory information or possess excessively high terminal computing power requirements, thereby rendering them difficult to deploy. In this study, a UAV recognition framework RaLMEN based on radar information is proposed, utilizing an ensemble learning architecture that incorporates BiLSTM and MLP. The framework integrates both temporal and static features extracted from aerial targets, aiming to provide a robust solution for low-altitude traffic surveillance. Experiments demonstrate that the proposed method requires only the temporal RCS information and position coordinates of a few trajectory points to accurately determine whether the target is a UAV. Furthermore, when evaluated with test data sampled from domains different from the training set, the framework demonstrates exceptional generalization performance, outperforming the baseline algorithms of all mainstream temporal neural networks.