Decentralized Estimation with Dependent Gaussian Observations

Fangrong Peng, Biao Chen

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This paper considers decentralized estimation with correlated noises under the Bayesian framework. For a tandem network with correlated additive Gaussian noises, we establish that threshold quantizers on local observations are optimal in the sense of maximizing Fisher information at the fusion center; this is true despite the fact that subsequent estimators may differ at the fusion center, depending on the statistical distribution of the parameter to be estimated. In addition, it is always beneficial to have the better sensor, i.e., the one with higher signal-To-noise ratio, serve as the fusion center in a tandem network. Finally, we identify different correlation regimes in terms of their impact on the estimation performance. These include the well-known case where negatively correlated noise benefits estimation performance as it facilitates noise cancellation, as well as two distinct regimes with positively correlated noises.

Original languageEnglish (US)
Article number7752980
Pages (from-to)1172-1182
Number of pages11
JournalIEEE Transactions on Signal Processing
Issue number5
StatePublished - Mar 1 2017


  • Decentralized estimation
  • communication direction
  • correlated observations
  • quantizer design
  • tandem network

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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