Event-triggered resilient distributed extended Kalman filter with consensus on estimation

This study investigates the event-triggered resilient recursive distributed state estimation problem for discrete-time nonlinear systems over sensor networks. An event-triggered mechanism is employed to save the limited computation resource and network bandwidth while maintaining the desired performance. A resilient Extended Kalman Filter (EKF) with consensus on estimations 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. By adopting the structure of the EKF, the filter gains are determined in each sensor node via utilization of an upper bound for the cross-covariance, thereby resulting in a lower computational burden. The boundedness of the estimation errors is analyzed. Simulation results are reported to illustrate the effectiveness of the proposed distributed filter.