2020 IEEE International Symposium
on Information Theory
21-26 June 2020 • Los Angeles, California, USA
| Paper ID | E.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. | ||
Plan Ahead
2021 IEEE International Symposium on Information Theory
11-16 July 2021 | Melbourne, Victoria, Australia
