Distributed Kalman filtering with data-driven communication

The paper deals with distributed Kalman filtering over a peer-to-peer sensor network with focus on a data transmission scheduling strategy aiming at reduced communication bandwidth and, consequently, at enhanced energy efficiency and prolonged network lifetime. A novel distributed Kalman filter algorithm with data-driven communication is devised relying on the idea that each node transmit its local information to the neighbors only when this is deemed to be particularly relevant for estimation purposes, i.e. whenever it significantly deviates from the information predicted from the last transmitted one. An interesting information-theoretic interpretation of the proposed strategy is presented and numerical simulations are provided to demonstrate its practical effectiveness.