Event-Based Estimation With Information-Based Triggering and Adaptive Update

Abstract

This paper is motivated by recent advancements of cyber-physical systems and significance of managing limited communication resources in their applications. We propose an open-loop estimation strategy with an information-based triggering mechanism coupled with an adaptive event-based fusion framework. In the open-loop topology considered in this paper, a sensor transfers its measurements to a remote estimator only in occurrence of specific events (asynchronously). Each event is identified using a local stochastic triggering mechanism without incorporation of a feedback from the remote estimator and/or implementation of a local filter at the sensor level. We propose a particular stochastic triggering criterion based on the projection of local observation into the state-space, which in turn is a measure of the achievable gain in the local information state vector. Then, we investigate an unsupervised fusion model at the estimation side where the estimator blindly listens to its communication channel without having a priori information of the triggering mechanism of the sensor. An update mechanism with a Bayesian collapsing strategy is proposed to adaptively form state estimates at the estimator side in an unsupervised fashion. The estimator is adaptive in the sense that it is able to distinguish between having received an actual measurement or noise. The simulation results show that the proposed information-based triggering mechanism significantly outperforms its counterparts specifically in low communication rates, and confirms the effectiveness of the proposed unsupervised fusion methodology.