Flight Data-Enabled Refined Anomaly Assessment of Unmanned Aerial Vehicles in Variable Trajectory Scenarios

Abnormal state assessment of unmanned aerial vehicles (UAVs) is crucial for flight safety and mission accomplishment. However, its practical application faces two significant challenges: the degradation of generalization performance under different trajectories and the roughness of 0–1 binary anomaly assessment. To this end, a variable trajectory-oriented refined anomaly assessment method for UAVs is proposed. This method, containing three distinctive modules, breaks through the limitations of traditional anomaly detection. A similarity-guided trajectory identification strategy is first established. This strategy achieves multitrajectory feature decoupling through feature space matching, enabling the construction of a trajectory specificity baseline model library. Additionally, a trajectory-oriented model adaptive optimization strategy is presented. This strategy employs dual-stage collaborative optimization, incorporating the baseline model’s preference and parameter dynamic adjustment, to enhance the generalization ability of models. To derive more refined and reliable assessment results, this study innovatively designs a quantitative assessment mechanism for abnormal states. Based on the ranking of abnormal state scores incorporating data deviation and uncertainty, a fine-grained identification of UAV abnormal states is realized. The experimental results using measured UAV flight data from different trajectories demonstrate that the proposed method significantly outperforms existing approaches in variable trajectory scenarios.