Distributed Parameter Estimation Under Event-triggered Communications

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.

Event-Triggered Distributed Estimation With Decaying Communication Rate

“…The results of this paper are significantly different from the literature. Regarding the step size, we remove the requirement that all sensors share the same setting [4][5][6][7][23][24][25][26], as well as the requirements of the concrete forms [4][5][6][24][25][26] and the monotonicity in [23]. The stationarity condition of observation matrices in [7] is removed.…”

“…Since sensor measurements and observation matrices are not shared between neighbors, our algorithm can be more suitable to the scenarios with privacy requirement than the diffusion estimators in [1,27]. Moreover, we generalize sensor communication topologies from undirected graphs [4][5][6]26] to a class of directed graphs, such as balanced digraphs containing a spanning tree.…”