  We consider the problem of distributed Kalman filtering for sensor networks
in the case there is a limit in data transmission and there is model
uncertainty. More precisely, we propose a distributed filtering strategy with
event-triggered communication in which the state estimators are computed
according to the least favorable model. The latter belongs to a ball (in
Kullback-Leibler topology) about the nominal model. We also present a
preliminary numerical example in order to test the performance of the proposed
strategy.
