Reliable Tracking With Intermittent Communications

We consider the problem of distributed target tracking of a linear dynamical system via networked sensors. Our setup consists of a set of sensors connected to a fusion center by means of communication links. Unreliable communication channels leads to communication delays and loss of information. To address this problem we model the arrival of messages from the sensors to the fusion center by a random process. The question arises as to what messages to encode for the fusion center. One possibility that has recently received much attention is to transmit local observations to the fusion center, which then fuses these intermittent observations through Kalman filtering techniques. In contrast we develop a scheme for fusion of intermittent local state estimates. The salient aspects of the scheme are: (a) efficiency, i.e., covariance of local estimate is no larger than covariance of observation; (b) robustness, i.e., the error covariance is bounded with high probability even under vanishing link-connectivity. In contrast, fusion of intermittent observations leads to unbounded errors even for moderate values for link-connectivity; (c) scalable performance, i.e., a K-node sensor network with K guaranteed communication links is inferior to an N-node sensor network with K functioning communication links on average