Event-triggered distributed fusion estimation with random transmission delays

Recently distributed fusion estimation problem has been widely studied because of better estimation accuracy, reliability and robustness. In this paper, an event-triggered distributed fusion estimation problem is investigated for a multi-sensor nonlinear networked system with random transmission delays. For each communication channel, an event-triggered scheduling mechanism is introduced to reduce excessive measurement transmission, and a D-length buffer is used to retrieve partly delayed measurements. Based on a sequential covariance intersection fusion technique, a distributed fusion estimation algorithm is designed utilizing local estimations calculated by modified unscented Kalman filter (UKF). Sufficient conditions are established to ensure boudedness of fusion estimation error covariance. Finally, comparative simulations indicate that measurement transmission is reduced for each communication channel while still maintaining satisfactory estimation performance by the proposed technique.