Single-Threshold Detection of a Random Signal in Noise with Multiple Independent Observations, Part 2: Continuous Case

Paul R. Prucnal, Malvin Carl Teich

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

A single-threshold detector is derived for a wide class of classical binary decision problems involving the likelihood-ratio detection of a signal embedded in noise. The class of problems considered encompasses the case of multiple independent (but not necessarily identically distributed) observations of a nonnegative (or nonpositive) signal embedded in additive and independent noise, where the range of the signal and noise is continuous. It is shown that a comparison of the sum of the observations with a unique threshold comprises an optimum detector if a weak condition on the noise is satisfied independent of the signal. Examples of noise densities that satisfy and that violate this condition are presented. A sufficient condition on the likelihood ratio which implies that the sum of the observations is also a sufficient statistic is considered.

Original languageEnglish (US)
Pages (from-to)213-218
Number of pages6
JournalIEEE Transactions on Information Theory
Volume25
Issue number2
DOIs
StatePublished - Mar 1979
Externally publishedYes

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Single-Threshold Detection of a Random Signal in Noise with Multiple Independent Observations, Part 2: Continuous Case'. Together they form a unique fingerprint.

  • Cite this