Event-Triggered Cooperative Unscented Kalman Filtering

Abstract

In this paper, the weighted average consensus-based unscented Kalman filtering combined with event-triggered communication mechanism is developed. Each sensor node chooses to transmit its latest measurement update to the corresponding remote estimator based on its own event-triggering condition. A sufficient condition is derived to guarantee that the estimation error is bounded in mean square. Finally, an illustrative example is presented to demonstrate the feasibility and effectiveness of the proposed algorithm.