Formation Target Intent Identification Based on a Static-Dynamic Collaborative Heterogeneous Graph Network

In complex maritime environments, formation intention recognition is fundamental to situation awareness and threat assessment tasks. Traditional methods rely on handcrafted rules and expert knowledge, making it difficult to jointly model inter-ship cooperative behaviors and dynamic sensing data. To address these challenges, this paper proposes a Static-Dynamic Collaborative Heterogeneous Graph Transformer network (SDC-HGT) for formation intention recognition. A static knowledge graph models ship attributes, onboard equipment, and inter-ship relationships, and a formation-level heterogeneous graph is constructed. A graph convolutional network aggregates static structural features, while a long short-term memory network extracts temporal features from radar and maneuvering behaviors. A gated fusion mechanism adaptively integrates static and dynamic features at the ship node level. Furthermore, a Heterogeneous Graph Transformer aggregates ship representations to produce a formation-level representation for intention classification. Experimental results demonstrate that SDC-HGT effectively fuses heterogeneous information, captures inter-ship cooperative behaviors, and achieves superior accuracy, robustness and anti-interference performance in extreme noise scenarios.