Distributed principal subspace estimation in wireless sensor networks

Lin Li, Anna Scaglione, Jonathan H. Manton

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


Motivated by applications in multi-sensor array detection and estimation, this paper studies the problem of tracking the principal eigenvector and the principal subspace of a signal's covariance matrix adaptively in a fully decentralized wireless sensor network (WSN). Sensor networks are traditionally designed to simply gather raw data at a fusion center, where all the processing occurs. In large deployments, this model entails high networking cost and creates a computational and storage bottleneck for the system. By leveraging both sensors' abilities to communicate and their local computational power, our objective is to propose distributed algorithms for principal eigenvector and principal subspace tracking. We show that it is possible to have each sensor estimate only the corresponding entry of the principal eigenvector or corresponding row of the $p$-dimensional principal subspace matrix and do so by iterating a simple computation that combines data from its network neighbors only. This paper also examines the convergence properties of the proposed principal eigenvector and principal subspace tracking algorithms analytically and by simulations.

Original languageEnglish (US)
Article number5719545
Pages (from-to)725-738
Number of pages14
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - Aug 2011
Externally publishedYes


  • Asynchronous time
  • average consensus
  • distributed algorithm
  • gossiping
  • signal detection
  • stochastic approximation
  • subspace estimation
  • subspace tracking
  • synchronous time

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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