This paper deals with the problem of quickest detection of a signal in discrete-time observations where the noise is not necessarily additive. By introducing a new cost function, penalizing the decision delay, in addition to penalizing wrong decisions as in the classical case, a global risk function is derived for use in a Bayesian framework. The minimization of the average risk leads to the optimum Bayesian decision regions, giving the structure of the optimum receiver. Some simplifications for elementary costs and some applications are investigated. The optimum receiver is shown to be a parallel bank of classical optimum filters, each one matched to a particular delay of the signal to be detected. Our approach is shown to apply to the detection of certain changes in a stochastic process.
Bayesian Quickest Signal Detection in a Discrete-Time Observation
IEEE Transactions on Aerospace and Electronic Systems ; AES-22 , 2 ; 170-176
1986-03-01
1279888 byte
Article (Journal)
Electronic Resource
English
Quickest Detection for Sequential Decentralized Decision Systems
Online Contents | 1996
|