State Estimation with Remote Sensors and Data-Driven Communication

This paper deals with the problem of estimating the state of a discrete-time linear stochastic dynamical system on the basis of data collected from multiple sensors subject to a limitation on the communication rate from the sensors. More specifically, the attention is devoted to a centralized sensor network and data-driven strategies for deciding when transmitting data from each sensor to the fusion node are considered. Sufficient conditions for the boundedness of the state covariance at the fusion node are given. Further, the possibility of determining a communication strategy with optimal performance in terms of minimum mean square estimation error at the fusion node is investigated.