A case study in robust quickest detection for hidden Markov models
Name
711203161-MIT.pdf
Description
Full printable version
Size
4.16 MB
Format
Adobe PDF
Checksum (MD5)
079cf9d29446d57e8b5b863a2ef2d285
Author(s)
Atwi, Aliaa
Advisor(s)
Munther A. Dahleh.
Alternative Title
Robust quickest detection for hidden Markov models
Date Issued
2010
Publisher
Massachusetts Institute of Technology
Abstract
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.
Description
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 65-66).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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