A greedy algorithm for decentralized Bayesian detection with feedback

Weiqiang Dong, Moshe Kam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


We consider a decentralized binary detection architecture comprised of n local detectors (LDs) communicating their decisions to a Data Fusion Center (DFC). Each local detector (LD) declares preference for one of two hypotheses (H0 or H1) and transmits it to the DFC. The decision of the kth LD at time step t is utk. The DFC develops a global preference ut0 for one of hypotheses based on the vector of local decisions Ut while minimizing a Bayesian cost. The input to the kth LD at time step t is the observation ytk collected from the surveyed environment, and the previous global decision ut-10. Alhakeem and Varshney developed a person-by-person optimal (PBPO) solution to this problem, namely a PBPO procedure to calculate the global fusion rule and the local decision rules. However, their solution requires that at each time step 2n fusion rule equations and 2n local threshold equations be solved simultaneously. In this paper we suggest a suboptimal solution to the problem, based on independent local minimizations of similar Bayesian cost by each LD and by the DFC. To assess the cost of decentralization, the performance of this solution is compared to that of an architecture that processes all observations in one central location.

Original languageEnglish (US)
Title of host publication37th IEEE Sarnoff Symposium, Sarnoff 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781509015405
StatePublished - Feb 7 2017
Event37th IEEE Sarnoff Symposium, Sarnoff 2016 - Newark, United States
Duration: Sep 19 2016Sep 21 2016

Publication series

Name37th IEEE Sarnoff Symposium, Sarnoff 2016


Other37th IEEE Sarnoff Symposium, Sarnoff 2016
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture


  • Data fusion
  • Decentralized detection detection
  • Decision fusion
  • Distributed detection
  • Fusion with feedback
  • Greedy algorithm


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