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. (C) 2019 Elsevier B.V. All rights reserved.