Distributed parameter estimation in networks

Kamiar Rahnama Rad, Alireza Tahbaz-Salehi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

51 Scopus citations

Abstract

In this paper, we present a model of distributed parameter estimation in networks, where agents have access to partially informative measurements over time. Each agent faces a local identification problem, in the sense that it cannot consistently estimate the parameter in isolation. We prove that, despite local identification problems, if agents update their estimates recursively as a function of their neighbors' beliefs, they can consistently estimate the true parameter provided that the communication network is strongly connected; that is, there exists an information path between any two agents in the network. We also show that the estimates of all agents are asymptotically normally distributed. Finally, we compute5 the asymptotic variance of the agents' estimates in terms of their observation models and the network topology, and provide conditions under which the distributed estimators are as efficient as any centralized estimator.

Original languageEnglish (US)
Title of host publication2010 49th IEEE Conference on Decision and Control, CDC 2010
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5050-5055
Number of pages6
ISBN (Print)9781424477456
DOIs
StatePublished - 2010
Event49th IEEE Conference on Decision and Control, CDC 2010 - Atlanta, United States
Duration: Dec 15 2010Dec 17 2010

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0191-2216

Conference

Conference49th IEEE Conference on Decision and Control, CDC 2010
Country/TerritoryUnited States
CityAtlanta
Period12/15/1012/17/10

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization

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