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arXiv · 1902.09825

Event-triggered distributed Bayes filter

Giorgio Battistelli·Luigi Chisci·Lin Gao·Daniela Selvi
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Abstract

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.

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Giorgio Battistelli, Luigi Chisci, Lin Gao, Daniela Selvi. 2019-02-26. Event-triggered distributed Bayes filter. https://arxiv.org/abs/1902.09825

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