Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy
Yuan‐Cheng Sun, Guang‐Hong Yang
Abstract
Abstract In this article, the problem of distributed state estimation is addressed for nonlinear systems based on the unscented Kalman filter (UKF) framework. The local estimates are obtained from distributed UKFs with stochastic event‐triggered schedules. The consensus of estimated states is achieved by employing the covariance intersection fusion method, while a novel event‐triggered strategy for the node‐to‐node communication is developed. Furthermore, the boundedness and convergence of the covariance matrix are analyzed. Compared with the existing works on distributed Kalman filtering, a new nonlinear global observability condition is proposed to relax the constraint that the system is necessarily observable by each sensor, and the relationship between the convergence and the event‐triggering parameters is revealed. Finally, simulations demonstrate the results in this article.