Bayesian Quickest Change Detection for Active Sensors

摘要

We consider the energy efficient quickest change detection problem for active sensors, which use radar, ultrasound, or optical sensing to control the information content in the sequentially collected noisy data. Controlling the extent of information contained in the collected data would lead to a cost or energy expenditure, where the cost would naturally increase as the extent of contained information increases. For such a sensor, our objective is to design control policies that optimally trade off performance metrics, such as detection delay, average cost expended in control, and probability of false alarm. We consider this problem in the setting of Bayesian quickest change detection. We propose two simple heuristic threshold policies, for which the total cost is shown to be close to the optimal through simulations and numerical studies.