Predictive and Self Triggering for Event-based State Estimation

TL;DR

This paper introduces predictive and self-triggering protocols for event-based state estimation that improve communication resource utilization while maintaining estimation quality, through a Bayesian decision framework.

Contribution

It proposes novel predictive and self-triggering schemes that enable better resource reallocation in event-based estimation, a significant advancement over traditional instant decision methods.

Findings

Effective trade-off between estimation accuracy and communication reduction.

Protocols enable better resource reallocation in networked estimation.

Numerical simulations demonstrate improved performance over existing methods.

Abstract

Event-based state estimation can achieve estimation quality comparable to traditional time-triggered methods, but with a significantly lower number of samples. In networked estimation problems, this reduction in sampling instants does, however, not necessarily translate into better usage of the shared communication resource. Because typical event-based approaches decide instantaneously whether communication is needed or not, free slots cannot be reallocated immediately, and hence remain unused. In this paper, novel predictive and self triggering protocols are proposed, which give the communication system time to adapt and reallocate freed resources. From a unified Bayesian decision framework, two schemes are developed: self-triggers that predict, at the current triggering instant, the next one; and predictive triggers that indicate, at every time step, whether communication will be needed at a given prediction horizon. The effectiveness of the proposed triggers in trading off estimation quality for communication reduction is compared in numerical simulations.