By Topic

Quickest detection of hidden Markov models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Biao Chen ; Connecticut Univ., Storrs, CT, USA ; Willett, P.

Page's test is optimal in quickly detecting distributional changes among independent observations. In this paper we propose a similar procedure for the quickest detection of dependent signals which can be conveniently modeled as hidden Markov models. Considering Page's test as a repeated sequential probability ratio test, we use Wald's approximation, with modification regarding the threshold overshoot, to predict the performance of the test, namely the average run length (ARL), between false alarms T. Using the asymptotic convergence property of the test statistic, we are also able to predict the ARL to detection D. The analysis shows that T is asymptotically exponential in D, as in the i.i.d. case. The results are supported by numerical examples

Published in:

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on  (Volume:4 )

Date of Conference:

10-12 Dec 1997