Abstract: Identification of Friend or Foe (IFF) for aircraft swarms is a critical prerequisite for cooperative operations and safe autonomous control, providing essential support for reliable decision-making and secure mission execution. Existing IFF approaches largely rely on radar signatures, communication identifiers, or dedicated identification devices, which often suffer from high deployment costs, vulnerability to interference, and limited interpretability. To address these limitations, this paper proposes a novel motion topology inference–based IFF method termed SRIFF (Swarm Relational Inference for Friend or Foe). First, an interpretable swarm feature modeling framework is constructed by integrating collective dynamics principles with graph-based representations, enabling physically interpretable reasoning of the IFF process and its underlying decision basis. Second, an adaptive temporal encoding mechanism is designed to accommodate variable-scale swarms, allowing unified representation and effective extraction of dynamic behavioral features. Finally, a variational inference framework with dual-decoder cooperative optimization is introduced to achieve end-to-end joint learning of topology inference, trajectory prediction, and IFF under multi-task constraints. Simulation results demonstrate that SRIFF exhibits stable and robust identification performance under complex interference and swarm scale variations, achieving superior accuracy and overall performance compared to existing methods. These results indicate that SRIFF provides an efficient, low-cost, and interpretable IFF solution for safety-critical swarm applications.

Key words: Aircraft Swarms, IFF(Identification of Friend or Foe), Motion Topology Inference, Adaptive Encoder, XAI(Explainable Arti-ficial Intelligence)