Quickest Change Detection with Cost-Constrained Experiment Design

In the classical quickest change detection problem, an observer performs only one experiment to monitor a stochastic process. This paper considers the case where, at each observation time, the decision-maker needs to choose between multiple experiments with different information qualities and costs. The goal is to minimize the worst-case average detection delay subject to false alarm and cost constraints. An algorithm called the 2E-CUSUM Algorithm has been developed to achieve this goal for the two-experiment case. Extensions to multiple-experiment designs are also studied, and 2E-CUSUM is extended accordingly. Data efficiency, where the observer has the choice not to perform an experiment, is explored as well. The proposed algorithms are analyzed and shown to be asymptotically optimal.