Detection of stochastic processes

Author

Kailath, Thomas ; Poor, H. Vincent

Author_Institution

Dept. of Electr. Eng., Stanford Univ., CA, USA

Volume

44

Issue

6

fYear

1998

fDate

10/1/1998 12:00:00 AM

Firstpage

2230

Lastpage

2231

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

Gaussian noise; correlation theory; dynamic programming; filtering theory; matched filters; maximum likelihood detection; optimisation; random noise; reviews; sequential estimation; signal detection; stochastic processes; RAKE receiver; continuous-time stochastic processes; cumulative-sum quickest detectors; dynamic optimization; estimator-correlator; likelihood ratio; matched filter; maximum-likelihood sequence detectors; multiuser detectors; noise; observation time; review; sequential probability ratio tests; signal detection theory; stochastic processes; Detectors; Fading; Matched filters; Mathematics; Maximum likelihood detection; Maximum likelihood estimation; Multipath channels; Signal detection; Signal to noise ratio; Stochastic processes;

fLanguage

English

Journal_Title

Information Theory, IEEE Transactions on

Publisher

ieee

ISSN

0018-9448

Type

jour

DOI

10.1109/18.720538

Filename

720538