Distributed Parameter Estimation Under Event-triggered Communications

TL;DR

This paper introduces an event-triggered communication scheme for distributed parameter estimation in multi-agent systems, significantly reducing communication while ensuring accurate and consistent estimates over time.

Contribution

It proposes a novel recursive estimator with an event-triggered protocol, providing design guidelines for thresholds to guarantee unbiasedness and consistency.

Findings

Communication is effectively reduced without sacrificing estimation accuracy.

The estimator achieves asymptotic unbiasedness and strong consistency.

Inter-trigger intervals tend to infinity, reducing communication frequency over time.

Abstract

In this paper, we study a distributed parameter estimation problem with an asynchronous communication protocol over multi-agent systems. Different from traditional time-driven communication schemes, in this work, data can be transmitted between agents intermittently rather than in a steady stream. First, we propose a recursive distributed estimator based on an event-triggered communication scheme, through which each agent can decide whether the current estimate is sent out to its neighbors or not. With this scheme, considerable communications between agents can be effectively reduced. Then, under mild conditions including a collective observability, we provide a design principle of triggering thresholds to guarantee the asymptotic unbiasedness and strong consistency. Furthermore, under certain conditions, we prove that, with probability one, for every agent the time interval between two successive triggered instants goes to infinity as time goes to infinity. Finally, we provide a numerical simulation to validate the theoretical results of this paper.