Linear coherent estimation with spatial collaboration

Swarnendu Kar, Pramod K. Varshney

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

A power-constrained sensor network that consists of multiple sensor nodes and a fusion center (FC) is considered, where the goal is to estimate a random parameter of interest. In contrast to the distributed framework, the sensor nodes may be partially connected, where individual nodes can update their observations by (linearly) combining observations from other adjacent nodes. The updated observations are communicated to the FC by transmitting through a coherent multiple access channel. The optimal collaborative strategy is obtained by minimizing the expected mean-square error subject to power constraints at the sensor nodes. Each sensor can utilize its available power for both collaboration with other nodes and transmission to the FC. Two kinds of constraints, namely the cumulative and individual power constraints, are considered. The effects due to imperfect information about observation and channel gains are also investigated. The resulting performance improvement is illustrated analytically through the example of a homogeneous network with equicorrelated parameters. Assuming random geometric graph topology for collaboration, numerical results demonstrate a significant reduction in distortion even for a moderately connected network, particularly in the low local signal-to-noise ratio regime.

Original languageEnglish (US)
Article number6470681
Pages (from-to)3532-3553
Number of pages22
JournalIEEE Transactions on Information Theory
Volume59
Issue number6
DOIs
StatePublished - May 29 2013

Keywords

  • Constrained optimization
  • distributed estimation
  • linear minimum mean square estimation (LMMSE)
  • wireless sensor networks

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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