Exploration into Optimal State Estimation with Event-triggered Communication

TL;DR

This paper introduces a novel event-triggered communication scheme for remote state estimation in stochastic linear systems, balancing performance and communication costs with theoretical guarantees and practical algorithms.

Contribution

It proposes a time-varying thresholding communication scheme, derives an optimal MMSE estimator, and formulates the problem within an MDP framework for optimal policy computation.

Findings

The new scheme reduces communication while maintaining estimation accuracy.

The derived MMSE estimator achieves near-optimal performance.

Simulation results confirm the effectiveness of the proposed approach.

Abstract

This paper deals with the problem of remote estimation of the state of a discrete-time stochastic linear system observed by a sensor with computational capacity to calculate local estimates. We design an event-triggered communication (ETC) scheme and a remote state estimator to optimally calibrate the tradeoff between system performance and limited communication resources. The novel communication scheme is the time-varying thresholding version for the cumulative innovation-driven communication scheme in [1], and its transmission probability is given. We derive the corresponding remote minimum mean square error (MMSE) estimator and present a tight upper bound for its MSE matrices. We also show that by employing a couple of weak assumptions, the optimality problem becomes (asymptotically) exact and can be addressed in an Markov Decision Process (MDP) framework, which delivers optimal policy and cost in an algorithmic procedure. The simulation results illustrate the effectiveness of our approach.