Event-based distributed set-membership filtering for localization of automatic guided vehicle under replay attacks

This paper explores a distributed remote localization framework for automatic guided vehicle (AGV) in the presence of unknown-but-bounded (UBB) noise and replay attacks. In order to improve flexibility of the localization process, mitigate the impact of replay attacks, and conserve network resources, an event-triggered distributed set-membership filtering (DSMF) method approach is developed for localization of AGV. Sufficient conditions guaranteeing the feasibility of the proposed DSMF method are established, while a recursive scheme is introduced for calculating refined local estimation ellipsoids that reliably cover the true AGV state. The simulation results confirm the validity of the proposed method. Meanwhile, it illustrates that the proposed event-triggered DSMF method can weaken the impact of replay attacks, reduce bandwidth occupancy, and enhance system adaptability in practical distributed environments.