Distributed Kalman Filtering over Sensor Networks with Unknown Random Link Failures

In this paper we consider the distributed consensus-based filtering problem for linear time-invariant systems over sensor networks subject to random link failures when the failure sequence is not known at the receiving side. We assume that the information exchanged, traveling along the channel, is corrupted by a noise and hence, it is no more possible to discriminate with certainty if a link failure has occurred. Therefore, in order to process the only significant information, we endow each sensor with detectors which decide on the presence of link failures. At each sensor the proposed approach consists of three steps: failure detection, local data aggregation and Kalman consensus filtering. Numerical examples show the effectiveness of this method.