Adaptive Bayesian decision fusion

Xiaoxun Zhu, Moshe Kam, Qiang Zhu

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


Design of parallel binary decision fusion systems is often performed under the assumption that the decision integrator (the data fusion center, DFC) possesses perfect knowledge of the local-detector (LD) statistics. In most studies, other statistical parameters are also assumed to be known, namely the a priori probabilities of the hypotheses, and the transition probabilities of the DFC-LD channels. The local observations are assumed to be statistically independent (conditioned on the hypothesis). Under these circumstances, the DFC's sufficient statistic is a weighted sum of the local decisions and the weights depend on the statistical parameters. When these parameters are unknown, we propose to estimate them on-line, using the discriminant function of the DFC as the performance index. This process can be performed with the help of a teacher that supplies the correct labels of the data, or without supervision, by estimating the correct labels from the data. The label-estimates are generated by the DFC, on the basis of its own past decisions and its assessment of the LD-data reliability.

Original languageEnglish (US)
Pages (from-to)5004-5009
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5) - San Diego, CA, USA
Duration: Dec 10 1997Dec 12 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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