Event-Triggered Estimation of Linear Systems: An Iterative Algorithm and Optimality Properties

TL;DR

This paper presents an iterative algorithm for optimal event-triggered estimation in linear systems, balancing estimation accuracy and communication cost, with proven convergence and demonstrated effectiveness through numerical examples.

Contribution

It introduces a novel iterative method for jointly designing estimators and event-triggers with proven convergence to optimal policies under certain conditions.

Findings

Algorithm converges to linear predictor and symmetric threshold policy.

Significant performance improvements for multimodal noise distributions.

Effective trade-off between estimation error and transmission rate achieved.

Abstract

This report investigates the optimal design of event-triggered estimation for first-order linear stochastic systems. The problem is posed as a two-player team problem with a partially nested information pattern. The two players are given by an estimator and an event-trigger. The event-trigger has full state information and decides, whether the estimator shall obtain the current state information by transmitting it through a resource constrained channel. The objective is to find an optimal trade-off between the mean squared estimation error and the expected transmission rate. The proposed iterative algorithm alternates between optimizing one player while fixing the other player. It is shown that the solution of the algorithm converges to a linear predictor and a symmetric threshold policy, if the densities of the initial state and the noise variables are even and radially decreasing functions. The effectiveness of the approach is illustrated on a numerical example. In case of a multimodal distribution of the noise variables a significant performance improvement can be achieved compared to a separate design that assumes a linear prediction and a symmetric threshold policy.