Sparse Spatial-Temporal Fusion Model for Flight Control System Real-Time Anomaly Detection

Real-time anomaly detection for flight control systems (FCSs) based on edge intelligence systems is essential for aerial vehicle safety. Extracting and fusing temporal evolution features and cross-dimensional spatial relations FCSs is an effective approach for anomaly detection. But existing models tend to achieve this through complex model structure with abundant parameters, which leads to substantial computational costs for real-time anomaly detection on resource-limited embedded systems. This article proposes a sparse hierarchical spatial-temporal fusion model for real-time anomaly detection of FCSs. First, a spatial feature extraction network is designed based on feature decoupling attention mechanism and weight-decay random forest to alleviate the dimensionality curse during the spatial correlation extraction. Second, a sparse temporal feature extraction network is designed based on multiscale temporal convolution and model-coupled sparsity regularization to reduce redundant parameters while preserving temporal modeling capability. Finally, anomaly detection thresholds are determined by statistically analyzing the prediction residuals to enhance the adaptability of the proposed method under dynamic operating conditions. The proposed method is validated using two real datasets from satellites and uncrewed aerial vehicles. Experimental results show average anomaly detection accuracy of 98.3% with false alarm rate of 5.0%. The model is then implemented on an embedded intelligence system, achieving an average single-step detection time of 1.22 ms. This demonstrates the potential of the proposed method for real-time anomaly detection in FCSs.