Event-Triggered State Estimation of High Dimensional Nonlinear Systems With Highly Nonlinear State Space Model Using Cubature Kalman Filter

Abstract

In this paper we design a state estimator which is proper for high dimensional nonlinear system with highly nonlinear state space model with noisy measurements over a wireless network using Cubature Kalman Filter (CKF). We show that by using the event-triggered cubature Kalman filter, the number of transmission through the communication channels between the measuring sensors and the remote state estimator will be reduced while the estimation quality can be guaranteed. An example shows the effectiveness of the proposed algorithm.