Detection of stochastic processes

被引：124
作者：
Kailath, T [1 ]
Poor, HV
机构：
[1] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA
[2] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 1998年 / 44卷 / 06期
基金：
美国国家科学基金会;
关键词：
dynamic programming; innovations processes; likelihood ratios; martingale theory; matched filters; optimal stopping; reproducing kernel Hilbert spaces; sequence detection; sequential methods; signal detection; signal estimation;
D O I：
10.1109/18.720538
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper reviews tno 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.
引用
收藏
页码：2230 / 2259
页数：30
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