Distributed and recursive parameter estimation in parametrized linear state-space models

S. Sundhar Ram, Venugopal V. Veeravalli, Angelia Nedich

Research output: Contribution to journalArticle

47 Citations (Scopus)

Abstract

We consider a network of sensors deployed to sense a spatio-temporal field and infer parameters of interest about the field. We are interested in the case where each sensor's observation sequence is modeled as a state-space process that is perturbed by random noise, and the models across sensors are parametrized by the same parameter vector. The sensors collaborate to estimate this parameter from their measurements, and to this end we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the on-line property of recursive prediction error algorithms.

Original languageEnglish (US)
Article number5373900
Pages (from-to)488-492
Number of pages5
JournalIEEE Transactions on Automatic Control
Volume55
Issue number2
DOIs
StatePublished - Feb 2010
Externally publishedYes

Fingerprint

Parameter estimation
Sensors

Keywords

  • Incremental recursive prediction error (IRPE)
  • Recursive prediction error (RPE)

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Distributed and recursive parameter estimation in parametrized linear state-space models. / Ram, S. Sundhar; Veeravalli, Venugopal V.; Nedich, Angelia.

In: IEEE Transactions on Automatic Control, Vol. 55, No. 2, 5373900, 02.2010, p. 488-492.

Research output: Contribution to journalArticle

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