A Knowledge-Guided Factor Graph Neural Network for Event Detection in Simulated Non-Cooperative Space Targets

Online abnormal event detection of non-cooperative space targets based on space-based optical observations is an urgent requirement for ensuring space security, particularly for sudden state changes such as attitude and orbit maneuver events. However, existing methods are dominated by ground-based monitoring and offline analysis, while space-based online methods are still under development and struggle to handle tasks under changing observation scales. To address this issue, we propose a Knowledge-Guided Factor Graph Neural Network (KG-FGNN). A heterogeneous graph is constructed to model high-order relationships among target properties, behaviors, observation conditions, and image features. Then, a dual-confidence dynamic weighting mechanism is designed to adaptively adjust the confidence of nodes and edges according to the observation scale. Finally, bidirectional temporal residuals are introduced to explicitly decouple sudden events from normal evolution using forward and backward prediction errors in an image sequence. Experimental results on simulated datasets demonstrate that KG-FGNN achieves higher detection accuracy for events compared to existing methods, while maintaining stable performance under cross-scale observation scenarios. This study provides an effective technical approach for online event detection in space-based optical image sequences and contributes to enhancing space situational awareness.