Abstract
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 (SPRT), 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. Analysis shows T is asymptotically exponential in D, as in the i.i.d. case. The results are supported by numerical examples.
Cite
CITATION STYLE
Chen, B., & Willett, P. (1997). Quickest detection of Hidden Markov Models. In Proceedings of the IEEE Conference on Decision and Control (Vol. 4, pp. 3984–3989). IEEE. https://doi.org/10.1109/cdc.1997.652487
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
