A Theoretical Framework for Event-Driven Correction in UAV Swarm Situational Awareness: Mechanism Design with Evidence-Theoretic Foundations

The effectiveness of unmanned aerial vehicle (UAV) swarms in complex and dynamic environments relies heavily on real-time and consistent situational awareness throughout the network. Effective event-driven correction mechanisms must meet two essential requirements: they must robustly handle uncertainties inherent in challenging situations and ensure strict commutativity between weighting and fusion operations to allow for distributed implementation. To tackle the critical issue of uncertain information processing, this work adopts Dempster–Shafer evidence theory because of its advantages in representing and managing epistemic uncertainty. However, the traditional discounting operation in evidence theory does not satisfy commutativity with the combination rule, which poses a significant barrier to distributed implementation. To address this limitation, we introduce a novel evidence weakening operation that is rigorously proven to be commutative with Dempster’s combination rule. This theoretical advancement enables the design of a distributed protocol that supports efficient propagation and parallel computation of corrections. Simulation results demonstrate that the proposed protocol achieves a zero correction error rate, along with approximately 40% reduction in latency and 35% savings in communication overhead compared to conventional serial discounting methods, while maintaining sublinear scalability. This approach provides a feasible solution for robust and efficient information fusion in dynamic multi-agent systems.