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
Distributed Kalman filtering with data-driven communication
Selvi D.
2016-01-01
Abstract
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 effectivenessI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
