A UAV swarm formation phase identification method based on knowledge-guided spatiotemporal representation

Unmanned Aerial Vehicle (UAV) swarms accomplish complex tasks through coordinated formation with their autonomy, timeliness, and intelligence. Phase identification for UAV swarm formation is a critical prerequisite for subsequent formation monitoring and performance assessment. However, accurate formation phase identification faces two main challenges. First, conventional phase definitions are diverse and complex, making identification difficult. Moreover, existing unsupervised learning methods yield inaccurate results due to insufficient extraction of high-dimension spatial and temporal coupling features of collaborative UAVs. To address these challenges, a UAV swarm formation phase identification method based on knowledge-guided spatiotemporal representation is proposed. Firstly, unified formation phases are defined and hierarchical identification subtasks are decomposed by knowledge-based task decomposition to improve phase separability and reduce potential misidentification. Then, formation phases are identified through a spatiotemporal representation model and a density-driven identification optimization mechanism. The model captures inter-vehicle spatial features, parameter couplings and intra-vehicle temporal features, while the optimization mechanism refines clustering results by mitigating high-frequency switching regions during transition phases. Finally, the proposed method is validated utilizing formation data generated by a UAV swarm formation simulation platform built on open-source software. Experimental results show that the proposed method achieves an average identification accuracy of 94.88% and an average F1 score of 0.965. Compared with other methods, the proposed method improves the average identification accuracy by 5.42% to 16.93% and the F1 score by 0.040 to 0.120, outperforming state-of-the-art methods. Consequently, ablation studies and comparative experiments demonstrate the informative spatiotemporal representation of the proposed method with superior performance.