Globally Optimal Decentralized Spatial Smoothing for Wireless Sensor Networks With Local Interactions

Abstract

In most sensor network applications, the vector containing the observations gathered by the sensors lies in a space of dimension equal to the number of nodes, typically because of observation noise, even though the useful signal belongs to a subspace of much smaller dimension. This motivates smoothing or rank reduction. We formulate a convex optimization problem, where we incorporate a fidelity constraint that prevents the final smoothed estimate from diverging too far from the observations. This leads to a distributed algorithm in which nodes exchange updates only with neighboring nodes. We show that the widely studied consensus algorithm is indeed only a very specific case of our more general formulation. Finally, we study the convergence rate and propose some approaches to maximize it.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2008
Accession Number
ADA505322

Entities

People

  • A. Swami
  • S. Barbarossa
  • T. Battisti

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Consensus Algorithms
  • Convergence
  • Detectors
  • Eigenvalues
  • Gaussian Noise
  • Measurement
  • Networks
  • Noise
  • Observation
  • Optimization
  • Polynomials
  • Reliability
  • Sensor Networks
  • Two Dimensional
  • Wireless Sensor Networks

Readers

  • Computer Networking
  • Computer Vision.
  • Systems Analysis and Design

Technology Areas

  • Space
  • Space - Space Objects