Anomaly Detection of Fixed-Wing Unmanned Aerial Vehicle (UAV) Based on Cross-Feature-Attention LSTM Network

With the gradual penetration of unmanned aerial vehicle (UAV) technology and its related applications in people’s lives, the safety of UAVs has become an important research focus. In this paper, we present an anomaly detection method based on cross-feature-attention LSTM neural networks. In an unsupervised setting, we use two types of networks to extract temporal and spatial features from flight data and predict future states to detect abnormal flight behavior. We conduct experiments on real flight data, the Air Lab Fault and Anomaly (ALFA) dataset, using multiple sets of different feature combinations. The results indicate that our method can maintain high performance across different feature combinations, achieving an average accuracy of 0.96 and a response time of 5.01 s.