Cooperative estimation in heterogeneous populations

Andrew J. Bean, Andrew Carl Singer

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

Abstract

We consider the problem of cooperative distributed estimation within a network of heterogeneous agents. We begin with the situation where each agent observes an independent stream of Bernoulli random variables, and the goal is for each to determine its own Bernoulli parameter. However, the agents of the population can be categorized into a small number of subgroups, where within each group the agents all have identical Bernoulli parameters. We present an algorithm for cooperative estimation in this setting which allows each agent's estimate to asymptotically converge to the correct value. We show how our technique can be applied in other settings, such as in heterogeneous least mean squares filter populations. Finally, we present simulation results showing the benefit of our technique, and compare it to noncooperative parameter estimation in a Bernoulli population.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages696-699
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

Keywords

  • adaptation
  • consensus
  • diffusion
  • distributed estimation
  • distributed signal processing
  • gossip algorithms

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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