A case study in robust quickest detection for hidden Markov models

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.