Detection of Gauss-Markov Random Field on Nearest-Neighbor Graph

Abstract

The problem of hypothesis testing against independence for a Gauss- Markov random field (GMRF) with nearest-neighbor dependency graph is analyzed. The sensors measuring samples from the signal field are placed IID according to the uniform distribution. The asymptotic performance of Neyman-Pearson detection is characterized through the large deviation theory. An expression for the error exponent is derived using a special law of large numbers for graph functionals. The exponent is analyzed for different values of the variance ratio and correlation. It is found that a more correlated GMRF has a higher exponent (improved detection performance) at low values of the variance ratio, whereas the opposite is true at high values of the ratio.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA489764

Entities

People

  • Anathram Swami
  • Anima Anandkumar
  • Lang Tong

Organizations

  • Cornell University College of Engineering

Tags

Communities of Interest

  • Materials and Manufacturing Processes
  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Covariance
  • Data Fusion
  • Data Science
  • Detection
  • Detectors
  • Electronic Mail
  • Engineering
  • False Alarms
  • Gaussian Processes
  • Image Processing
  • Information Science
  • Probability
  • Random Variables
  • Signal Processing
  • Social Sciences
  • Statistics

Readers

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  • Approximation Theory.
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