Technical Program

Paper Detail

Paper IDE.1.4
Paper Title Multislot and Multistream Quickest Change Detection in Statistically Periodic Processes
Authors Taposh Banerjee, University of Texas at San Antonio, United States; Prudhvi Gurram, CCDC Army Research Lab and Booz Allen Hamilton, United States; Gene Whipps, CCDC Army Research Lab, United States
Session E.1: Detection Theory
Presentation Lecture
Track Detection and Estimation
Manuscript  Click here to download the manuscript
Virtual Presentation  Click here to watch in the Virtual Symposium
Abstract Mixture-based algorithms are proposed for detecting a change in the distribution of a statistically periodic process in multislot and multistream settings. In the multislot change detection problem, the distribution of the observed process can change in any subset of time slots in each period. In the multistream change detection problem, there are parallel streams of observations, and the change can affect an arbitrary subset of streams. It is shown that the algorithms are asymptotically optimal in a Bayesian setting.

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2021 IEEE International Symposium on Information Theory

11-16 July 2021 | Melbourne, Victoria, Australia

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