Interactive distributed detection: Architecture and performance analysis

Earnest Akofor, Biao Chen

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

This paper studies the impact of interactive fusion on detection performance in tandem fusion networks with conditionally independent observations. Within the Neyman-Pearson framework, two distinct regimes are considered: 1) the fixed sample size test and 2) the large sample test. For the former, it is established that interactive distributed detection may strictly outperform the one-way tandem fusion structure. However, for the large sample regime, it is shown that interactive fusion has no improvement on the asymptotic performance characterized by the Kullback-Leibler distance compared with the simple one-way tandem fusion. The results are then extended to interactive fusion systems where the fusion center and the sensor may undergo multiple steps of memoryless interactions or that involve multiple peripheral sensors, as well as to interactive fusion with soft sensor outputs.

Original languageEnglish (US)
Article number6874577
Pages (from-to)6456-6473
Number of pages18
JournalIEEE Transactions on Information Theory
Volume60
Issue number10
DOIs
StatePublished - Oct 1 2014

Keywords

  • Decision theory
  • Kullback-Leibler distance
  • Neyman-Pearson test
  • distributed detection
  • interactive fusion

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences

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