Event-Triggered Robust Fusion Estimation for Multisensor Nonlinear Cyber-Physical Systems Under Denial-of-Service Attacks

Focusing on event-triggered robust state estimation, this study examines nonlinear networked systems under denial-of-service attacks and limited communication rates. Linearization errors arising in the extended Kalman filter increase state-estimation error, and denial-of-service–induced network overloads cause packet dropouts that disrupt remote robust estimation and degrade measurement delivery. To alleviate these effects, we develop an event-triggered robust state estimator and establish, under standard observability, packet-arrival, and trigger-threshold conditions, the uniform mean-square boundedness of the estimation error, with explicit upper bounds and an admissible design range. Numerical simulations demonstrate that the proposed estimator markedly improves estimation accuracy.