Noise enhanced nonparametric detection

Hao Chen, Pramod K. Varshney, Steven Kay, James H. Michels

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.

Original languageEnglish (US)
Pages (from-to)499-506
Number of pages8
JournalIEEE Transactions on Information Theory
Issue number2
StatePublished - 2009


  • Hypothesis testing
  • Noise enhanced detection
  • Nonparametric detection

ASJC Scopus subject areas

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


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