A Semidefinite Programming Approach to Source Localization in Wireless Sensor Networks

Abstract

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

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Document Details

Document Type
Technical Report
Publication Date
May 16, 2008
Accession Number
ADA500038

Entities

People

  • Chen Meng
  • Soura Dasgupta
  • Zhi Ding

Organizations

  • University of California

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programming
  • Convergence
  • Convex Sets
  • Detectors
  • Electronic Mail
  • Engineering
  • Maximum Likelihood Estimation
  • Measurement
  • Networks
  • Noise
  • Optimization
  • Semidefinite Programming
  • Sensor Networks
  • Signal Processing
  • Simulations
  • Wireless Sensor Networks

Fields of Study

  • Engineering

Readers

  • Artificial Intelligence
  • Graph Algorithms and Convex Optimization.
  • Sensor Fusion and Tracking Systems.