The aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, en- ergy consumption in the context of distributed state estimation over a peer-to-peer sensor network. Specifically, a distributed Bayes filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when the Kullback-Leibler divergence between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. The stability of the proposed event-triggered distributed Bayes filter is proved in the linear-Gaussian (Kalman filter) case. The performance of the proposed algorithm is also evaluated through simulation experiments concerning a target tracking application.

Event-triggered distributed Bayes filter / Giorgio Battistelli, Luigi Chisci, Lin Gao, Daniela Selvi. - CD-ROM. - (2019), pp. 2731-2736. (Intervento presentato al convegno European Control Conference 2019 tenutosi a Napoli, Italia nel 25-28 Giugno 2019).

Event-triggered distributed Bayes filter

Giorgio Battistelli;Luigi Chisci;Lin Gao;Daniela Selvi
2019

Abstract

The aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, en- ergy consumption in the context of distributed state estimation over a peer-to-peer sensor network. Specifically, a distributed Bayes filter with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when the Kullback-Leibler divergence between the current local posterior and the one predictable from the last transmission exceeds a preset threshold. The stability of the proposed event-triggered distributed Bayes filter is proved in the linear-Gaussian (Kalman filter) case. The performance of the proposed algorithm is also evaluated through simulation experiments concerning a target tracking application.
2019
European Control Conference 2019
European Control Conference 2019
Napoli, Italia
25-28 Giugno 2019
Giorgio Battistelli, Luigi Chisci, Lin Gao, Daniela Selvi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1161702
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