Event-triggered distributed Bayes filter
For distributed sensor networks, this work reduces communication overhead while maintaining estimation accuracy, though it is an incremental extension of existing event-triggered methods.
The paper develops an event-triggered distributed Bayes filter that reduces communication bandwidth and energy consumption in peer-to-peer sensor networks by transmitting only when the Kullback-Leibler divergence exceeds a threshold. Stability is proved for linear-Gaussian cases, and simulations show reduced communication without significant performance loss.
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.