Error Bounds for Parallel Distributed Detection under the Neyman-Pearson Criterion

Po-Ning Chen, Adrian Papamarcou

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The Neyman-Pearson performance of a distributed detection system is considered wherein n independent and identically distributed observations are quantized locally into M-ary messages and transmitted to a fusion center. Under fairly general assumptions, it is shown that the type II error probability achieved by the best identical-quantizer system is at most a fixed (in n) multiple of that achieved by the absolutely optimal system.

Original languageEnglish
Pages (from-to)528-533
Number of pages6
JournalIEEE Transactions on Information Theory
Volume41
Issue number2
DOIs
StatePublished - 1 Jan 1995

Keywords

  • asymptotic expansions
  • Distributed detection
  • error exponents
  • large deviations
  • quantization

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