A case study in robust quickest detection for hidden Markov models

Quickest Detection is the problem of detecting abrupt changes in the statistical behavior of an observed signal in real-time. The literature has focused much attention on the problem for i.i.d. observations. In this thesis, we assess the feasibility of two HMM quickest detection frameworks recently suggested for detecting rare events in a real data set. The first method is a dynamic programming based Bayesian approach, and the second is a non-Bayesian approach based on the cumulative sum algorithm. We discuss implementation considerations for each method and show their performance through simulations for a real data set. In addition, we examine, through simulations, the robustness of the non-Bayesian method when the disruption model is not exactly known but belongs to a known class of models.

Department dc:contributor.department

Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2010

Author dc:creator

• Atwi, Aliaa

Advisor dc:contributor.advisor

• Munther A. Dahleh.

• Electrical Engineering and Computer Science.

Statement dc:rights

• M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/62461

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/62461