Repository logo

Infoscience

  • English
  • French
Log In
Logo EPFL, École polytechnique fédérale de Lausanne

Infoscience

  • English
  • French
Log In
  1. Home
  2. Academic and Research Output
  3. Journal articles
  4. Optimal Linear Fusion for Distributed Detection Via Semidefinite Programming
 
research article

Optimal Linear Fusion for Distributed Detection Via Semidefinite Programming

Quan, Zhi
•
Ma, Wing-Kin
•
Cui, Shuguang
Show more
2010
IEEE Transactions on Signal Processing

Consider the problem of signal detection via multiple distributed noisy sensors. We study a linear decision fusion rule of [Z. Quan, S. Cui, and A. H. Sayed, ¿Optimal Linear Cooperation for Spectrum Sensing in Cognitive Radio Networks,¿ IEEE J. Sel. Topics Signal Process., vol. 2, no. 1, pp. 28-40, Feb. 2008] to combine the local statistics from individual sensors into a global statistic for binary hypothesis testing. The objective is to maximize the probability of detection subject to an upper limit on the probability of false alarm. We propose a more efficient solution that employs a divide-and-conquer strategy to divide the decision optimization problem into two subproblems. Each subproblem is a nonconvex program with a quadratic constraint. Through a judicious reformulation and by employing a special matrix decomposition technique, we show that the two nonconvex subproblems can be solved by semidefinite programs in a globally optimal fashion. Hence, we can obtain the optimal linear fusion rule for the distributed detection problem. Compared with the likelihood-ratio test approach, optimal linear fusion can achieve comparable performance with considerable design flexibility and reduced complexity.

  • Details
  • Metrics
Type
research article
DOI
10.1109/TSP.2009.2039823
Author(s)
Quan, Zhi
Ma, Wing-Kin
Cui, Shuguang
Sayed, Ali H.  
Date Issued

2010

Publisher

IEEE

Published in
IEEE Transactions on Signal Processing
Volume

58

Issue

4

Start page

2431

End page

2436

Editorial or Peer reviewed

REVIEWED

Written at

OTHER

EPFL units
ASL  
Available on Infoscience
December 19, 2017
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/142966
Logo EPFL, École polytechnique fédérale de Lausanne
  • Contact
  • infoscience@epfl.ch

  • Follow us on Facebook
  • Follow us on Instagram
  • Follow us on LinkedIn
  • Follow us on X
  • Follow us on Youtube
AccessibilityLegal noticePrivacy policyCookie settingsEnd User AgreementGet helpFeedback

Infoscience is a service managed and provided by the Library and IT Services of EPFL. © EPFL, tous droits réservés