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Multiscale Spatio-Temporal Graph Convolutional Network for UAV Anomaly Detection
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DOI：10.1109/JIOT.2026.3661961.png)
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Abstract
En 中文
With the development of sensor and communication technologies, unmanned aerial vehicles (UAVs) have been widely applied in various critical fields. UAV anomaly detection (AD) with flight data can quickly identify system failures and prevent major accidents and losses. Existing methods typically ignore scale variations and intricate spatio-temporal dependencies inherent in-flight data, and they depend on fixed thresholds that often produce high false alarms. To address these issues, we propose a multiscale spatio-temporal graph convolutional network (MSTGCNet) for UAV AD. First, the graph-enhanced mixture-of-experts (GMoE) block uses seasonal-trend decomposition to route each input sequence to the expert network (EN) whose patch-based receptive field best matches its temporal scale, enabling effective multiscale feature extraction. Second, the causally constrained spatio-temporal GCN (CC-STGCN) jointly learns an adaptive spatio-temporal graph and applies graph convolutions to capture dependencies across sensors and time. Third, we devise an adaptive thresholding strategy for streaming data (ATSSD) that dynamically adjusts detection thresholds within a sliding window based on local statistics, reducing false positives. Experiments on three real-world UAV flight datasets demonstrate that MSTGCNet significantly outperforms state-of-the-art baselines in $F1$ score, showcasing its excellent overall performance, and the ablation study validates the effectiveness of the model’s key components. Finally, parameter sensitivity analysis and visualization experiments provide deep insights into the proposed model and the characteristics of the flight data. The code is available at https://github.com/SteelHu/MSTGCNet
Keywords:
Adaptive threshold
anomaly detection (AD)
graph convolutional network
multiscale modeling
unmanned aerial vehicles (UAVs)
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