Event-triggered distributed Kalman filter with consensus on estimation for state-saturated systems

This study investigates the problem of event-triggered distributed state estimation for discrete-time, nonlinear systems with state saturation. A Kalman-like filter is developed, and consensus is first achieved with respect to the prediction estimation. The accuracy of the computed estimation is then improved via two recursive equations. The filter gains are determined in each sensor node via utilization of only an upper bound for the common error covariance, thereby resulting in a lower computational burden. Finally, the boundedness of the estimation errors is analyzed, and a comparison of the simulation results demonstrates that the proposed filtering method outperforms a recent rival method.