A distributed diffusion Kalman filter with event-triggered mechanism and guaranteed stability

In this article, a distributed diffusion Kalman filtering algorithm with event-triggered communication (DDKF-E) is studied for discrete-time nonlinear systems. According to the event-triggered communication protocol, the data between sensors and estimators are transmitted only when the predefined conditions are satisfied. Considering the characteristic of event-triggered method and truncated error by linearization, an upper bound of the estimation error covariance matrix is obtained by using the variance-constrained method. The Kalman gain is designed to minimize the upper bound and then two Riccati equations are obtained. Furthermore, the stochastic stability theory is used to prove the stability of DDKF-E, and it is derived that the estimation error of DDKF-E is exponentially bounded in mean square. Finally, numerical simulations validate the effectiveness of the DDKF-E algorithm.