Fusion of threshold rules for target detection in wireless sensor networks

Mengxia Zhu, Song Ding, Qishi Wu, R. R. Brooks, N. S.V. Rao, S. S. Iyengar

Research output: Contribution to journalArticle

40 Scopus citations

Abstract

We propose a binary decision fusion rule that reaches a global decision on the presence of a target by integrating local decisions made by multiple sensors. Without requiring a priori probability of target presence, the fusion threshold bounds derived using Chebyshev's inequality ensure a higher hit rate and lower false alarm rate compared to the weighted averages of individual sensors. The Monte Carlo-based simulation results show that the proposed approach significantly improves target detection performance, and can also be used to guide the actual threshold selection in practical sensor network implementation under certain error rate constraints.

Original languageEnglish
Article number18
JournalACM Transactions on Sensor Networks
Volume6
Issue number2
DOIs
StatePublished - 1 Feb 2010

    Fingerprint

Keywords

  • Binary decision fusion
  • Chebyshev inequality
  • False alarm rate
  • Hit rate
  • ROC curve
  • Wireless sensor network

Cite this