Distributed M-ary hypothesis testing with binary local decisions

Xiaoxun Zhu, Yingqin Yuan, Chris Rorres, Moshe Kam

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


Parallel distributed detection schemes for M-ary hypothesis testing often assume that for each observation the local detector transmits at least log 2M bits to a data fusion center (DFC). However, it is possible for less than log2M bits to be available, and in this study we consider 1-bit local detectors with M>2. We develop conditions for asymptotic detection of the correct hypothesis by the DFC, formulate the optimal decision rules for the DFC, and derive expressions for the performance of the system. Local detector design is demonstrated in examples, using genetic algorithm search for local decision thresholds. We also provide an intuitive geometric interpretation for the partitioning of the observations into decision regions. The interpretation is presented in terms of the joint probability of the local decisions and the hypotheses.

Original languageEnglish (US)
Pages (from-to)157-167
Number of pages11
JournalInformation Fusion
Issue number3
StatePublished - Sep 2004
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture


  • Decision fusion
  • Distributed hypothesis testing


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