Semi-Definite Programming Relaxation for Non-Line-of-Sight Localization

Abstract

We consider the problem of estimating the locations of a set of points in a k-dimensional euclidean space given a subset of the pairwise distance measurements between the points. We focus on the case when some fraction of these measurements can be arbitrarily corrupted by large additive noise. Given that the problem is highly non-convex, we propose a simple semidefinite programming relaxation that can be efficiently solved using standard algorithms. We define a notion of non-contractibility and show that the relaxation gives the exact point locations when the underlying graph is non-contractible. The performance of the algorithm is evaluated on an experimental data set obtained from a network of 44 nodes in an indoor environment and is shown to be robust to non-line-of-sight errors.

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Document Details

Document Type
Technical Report
Publication Date
Aug 18, 2012
Accession Number
ADA566684

Entities

People

  • Giulia Fanti
  • Kannan Ramchandran
  • Venakatesan Ekambaram

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Cyber

DTIC Thesaurus Topics

  • Additives (Chemicals)
  • Algorithms
  • Computer Programming
  • Computer Science
  • Data Sets
  • Electrical Engineering
  • Engineering
  • Environment
  • Errors
  • Experimental Data
  • Gaussian Noise
  • Line Of Sight
  • Measurement
  • Noise
  • Optimization
  • Simulations
  • Standards

Fields of Study

  • Computer science

Readers

  • Graph Algorithms and Convex Optimization.
  • Operations Research
  • Radar Systems Engineering.

Technology Areas

  • Space