Detection of stochastic processes

Thomas Kailath, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

135 Scopus citations

Abstract

This paper reviews two streams of development, from the 1940's to the present, in signal detection theory: the structure of the likelihood ratio for detecting signals in noise and the role of dynamic optimization in detection problems involving either very large signal sets or the joint optimization of observation time and performance. This treatment deals exclusively with basic results developed for the situation in which the observations are modeled as continuous-time stochastic processes. The mathematics and intuition behind such developments as the matched filter, the RAKE receiver, the estimator-correlator, maximum-likelihood sequence detectors, multiuser detectors, sequential probability ratio tests, and cumulative-sum quickest detectors, are described.

Original languageEnglish (US)
Pages (from-to)2230-2259
Number of pages30
JournalIEEE Transactions on Information Theory
Volume44
Issue number6
DOIs
StatePublished - 1998

All Science Journal Classification (ASJC) codes

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

Keywords

  • Dynamic programming
  • Innovations processes
  • Likelihood ratios
  • Matched filters
  • Mmartingale theory
  • Optimal stopping
  • Reproducing kernel Hubert spaces
  • Sequence detection
  • Sequential methods
  • Signal detection
  • Signal estimation

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