Distributed linear parameter estimation: Asymptotically efficient adaptive strategies

Soummya Kart, José M.F. Moura, H. Vincent Poor

Research output: Contribution to journalArticle

41 Scopus citations

Abstract

This paper considers the problem of distributed adaptive linear parameter estimation in multiagent inference networks. Local sensing model information is only partially available at the agents, and interagent communication is assumed to be unpredictable. The paper develops a generic mixed time-scale stochastic procedure consisting of simultaneous distributed learning and estimation, in which the agents adaptively assess their relative observation quality over time and fuse the innovations accordingly. Under rather weak assumptions on the statistical model and the interagent communication, it is shown that, by properly tuning the consensus potential with respect to the innovation potential, the asymptotic information rate loss incurred in the learning process may be made negligible. As such, it is shown that the agent estimates are asymptotically efficient, in that their asymptotic covariance coincides with that of a centralized estimator (the inverse of the centralized Fisher information rate for Gaussian systems) with perfect global model information and having access to all observations at all times. The proof techniques are mainly based on convergence arguments for non-Markovian mixed time-scale stochastic approximation procedures. Several approximation results developed in the process are of independent interest.

Original languageEnglish (US)
Pages (from-to)2200-2229
Number of pages30
JournalSIAM Journal on Control and Optimization
Volume51
Issue number3
DOIs
StatePublished - Oct 7 2013

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Applied Mathematics

Keywords

  • Adaptive algorithms
  • Asymptotically efficient
  • Distributed estimation
  • Mixed time-scale
  • Multiagent systems
  • Stochastic approximation

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