The paper deals with distributed Kalman filtering over a peer-to-peer sensor network with focus on a data trans- mission 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 Daniela
2016-01-01

Abstract

The paper deals with distributed Kalman filtering over a peer-to-peer sensor network with focus on a data trans- mission 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.
2016
Distributed estimation. Kalman filter. Sensor networks. Data-driven communication. Sensor fusion.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/7002
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