Event-triggered distributed state estimation over wireless sensor networks

This paper focuses on the event-triggered distributed state estimation over sensor networks, in which each sensor node selectively transmits the local information to its neighbors for the reduced communication bandwidth and the prolonged network lifetime. Based on an individual stochastic triggering condition, an event-triggered minimum mean square error (MMSE) estimator is proposed in a recursive form, and then an event-triggered distributed state estimation algorithm is developed by repeatedly fusing the local information and the event-triggered information. It is shown that, under network connectivity, collective observability and large enough triggering parameters, the distributed estimator in each sensor node is stable with the uniformly bounded estimation error in mean square. Finally, a target tracking example is provided to illustrate the practical effectiveness of the proposed technique.