Title :
Detection of stochastic processes
Author :
Kailath, Thomas ; Poor, H. Vincent
Author_Institution :
Dept. of Electr. Eng., Stanford Univ., CA, USA
fDate :
10/1/1998 12:00:00 AM
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;
Journal_Title :
Information Theory, IEEE Transactions on
