# Event-triggered distributed Bayes filter

## TL;DR

This paper introduces an event-triggered distributed Bayes filter that reduces communication and energy use in sensor networks by transmitting information only when significant divergence occurs, with proven stability in linear-Gaussian cases.

## Contribution

It develops a novel event-triggered communication strategy for distributed Bayes filters, improving efficiency while maintaining stability in linear-Gaussian scenarios.

## Key findings

- Reduces communication bandwidth in sensor networks.
- Proves stability of the filter in linear-Gaussian cases.
- Demonstrates effectiveness through target tracking simulations.

## 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.