MoE-Based Anomaly Detection of High-Altitude UAV Flight Data

Analyzing flight data from high-altitude unmanned aerial vehicles (UAVs) is an essential approach to anomaly detection and early warning. However, flight data collected during the climb, cruise, and descent phases of high-altitude UAV missions exhibit pronounced distributional differences, making it difficult for existing anomaly detection methods to establish a unified model of normal flight behavior across all phases, thereby constraining detection accuracy. To address this, this paper proposes an anomaly detection method for high-altitude UAV flight data based on a Mixture of Experts (MoE). Specifically, a Multi-Scale Dual-Path Mixture-of-Experts encoder is constructed to extract local perturbations from UAV flight sequences; multi-scale convolution, a Transformer, and a dual-path LSTM are fused to capture temporal dependencies within the flight data; and a gating network automatically adapts to and uniformly models the data distributions across different mission phases. Furthermore, a two-stage reconstruction error is employed to construct sliding-window anomaly scores, and the median absolute deviation (MAD) is adopted to adaptively determine the anomaly decision threshold, enabling detection of flight-data anomalies across all mission phases. Experimental results on a self-constructed high-altitude UAV flight dataset with controlled anomaly injections demonstrate that the proposed method achieves a window F1 of 93.97% and an event recall of 91.77% across three independent training seeds.