Event-triggered cooperative unscented Kalman filtering and its application in multi-UAV systems

This paper proposes a novel consensus-based distributed unscented Kalman filtering algorithm with event-triggered communication mechanisms. With such an algorithm, each sensor node transmits the newest measurement to the corresponding remote estimator selectively on the basis of its own event-triggering condition. Compared to the existing approaches, the proposed algorithm can significantly reduce unnecessary data transmissions and hence save communication energy consumption and alleviate the communication burden. A sufficient condition is provided to guarantee the stochastic stability of the distributed nonlinear filtering scheme. The proposed algorithm is applicable to a wide range of distributed estimation tasks, e.g., tracking a moving target with multiple unmanned aerial vehicles (UAVs). Simulation results demonstrate the feasibility and effectiveness of the proposed filtering algorithm.