The aim of this paper is to devise a strategy that is able to reduce communication bandwidth and, consequently, energy 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 eventtriggered 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.
@article{arxiv.1902.09825,
title = {Event-triggered distributed Bayes filter},
author = {Giorgio Battistelli and Luigi Chisci and Lin Gao and Daniela Selvi},
journal= {arXiv preprint arXiv:1902.09825},
year = {2019}
}