Detection of stochastic processes

Thomas Kailath; H. Vincent Poor

doi:10.1109/18.720538

Detection of stochastic processes

Thomas Kailath, H. Vincent Poor

Research output: Contribution to journal › Article › peer-review

150 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 language	English (US)
Pages (from-to)	2230-2259
Number of pages	30
Journal	IEEE Transactions on Information Theory
Volume	44
Issue number	6
DOIs	https://doi.org/10.1109/18.720538
State	Published - 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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10.1109/18.720538

Cite this

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title = "Detection of stochastic processes",

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.",

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",

author = "Thomas Kailath and Poor, {H. Vincent}",

note = "Funding Information: Manuscript received February 28, 1998; revised June 7, 1998. This work was supported in part by the Air Force Office of Scientific Research, the Army Research Office, DARPA, the National Science Foundation, and the Office of Naval Research.",

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AU - Kailath, Thomas

AU - Poor, H. Vincent

N1 - Funding Information: Manuscript received February 28, 1998; revised June 7, 1998. This work was supported in part by the Air Force Office of Scientific Research, the Army Research Office, DARPA, the National Science Foundation, and the Office of Naval Research.

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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.

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KW - Innovations processes

KW - Likelihood ratios

KW - Matched filters

KW - Mmartingale theory

KW - Optimal stopping

KW - Reproducing kernel Hubert spaces

KW - Sequence detection

KW - Sequential methods

KW - Signal detection

KW - Signal estimation

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Detection of stochastic processes

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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