Robust distributed Kalman filtering with event-triggered communication

We consider the problem of distributed Kalman filtering for sensor networks in the case there are constraints in data transmission, necessary to deal with the limitation of the channel capacity, and there is model uncertainty. More precisely, we propose two distributed filtering strategies with event-triggered communication where the state estimators are computed according to the least favorable model. The latter belongs to a ball about the nominal model. We also show that both the methods are stable in the sense that the mean-square of the state estimation error is bounded in all the nodes.