Remote State Estimation with Stochastic Event-triggered Sensor Schedule in the Presence of Packet Drops

TL;DR

This paper addresses remote state estimation for linear systems using stochastic event-triggered sensors with packet drops, deriving optimal and sub-optimal estimators and analyzing their performance.

Contribution

It introduces a novel framework for state estimation with stochastic sensor schedules considering packet drops, including derivation of MMSE estimators and complexity-reducing sub-optimal methods.

Findings

Optimal estimators are computationally intensive, requiring exponential resources.

Sub-optimal estimators significantly reduce complexity with minimal performance loss.

Simulations demonstrate the effectiveness of the proposed estimators under packet drops.

Abstract

This paper studies the remote state estimation problem of linear time-invariant systems with stochastic event-triggered sensor schedules in the presence of packet drops between the sensor and the estimator. It is shown that the system state conditioned on the available information at the estimator side is Gaussian mixture distributed. Minimum mean square error (MMSE) estimators are subsequently derived for both open-loop and closed-loop schedules. Since the optimal estimators require exponentially increasing computation and memory, sub-optimal estimators to reduce the computational complexities are further provided. In the end, simulations are conducted to illustrate the performance of the optimal and sub-optimal estimators.