2009/10/24 by Olympia Hadjiliadis, Tobias Schaefer, H. Vincent Poor
Computer Science · Decision Sciences · Engineering · Mathematics · #Advanced Statistical Process Monitoring #Algorithm #Artificial intelligence #Asymptotically optimal algorithm #CUSUM #Computer science #Constraint (computer-aided design) #Control theory (sociology) #Data mining #Detection theory #Detector #Divergence (linguistics) #Fault Detection and Control Systems #Kullback–Leibler divergence #Mathematical optimization #Mathematics #Measure (data warehouse) #Real-time computing #SIGNAL (programming language) #Scientific Measurement and Uncertainty Evaluation #Statistics #Stochastic process #Upper and lower bounds #cs.IT #math.IT
paper · pdf · doi:10.1109/cdc.2009.5400871
6 pages, 48th IEEE Conference on Decision and Control, Shanghai 2009 December 16 - 18
arxiv created 2009/10/24 · openalex publication_date 2009/12/01 · arxiv updated 2016/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This work considers the problem of quickest detection of signals in a coupled system of N sensors, which receive continuous sequential observations from the environment. It is assumed that the signals, which are modeled a general Ito¿ processes, are coupled across sensors, but that their onset times may differ from sensor to sensor. The objective is the optimal detection of the first time at which any sensor in the system receives a signal. The problem is formulated as a stochastic optimization problem in which an extended average Kullback-Leibler divergence criterion is used as a measure of detection delay, with a constraint on the mean time between false alarms. The case in which the sensors employ cumulative sum (CUSUM) strategies is considered, and it is proved that the minimum of N CUSUMs is asymptotically optimal as the mean time between false alarms increases without bound.