Allerton 2015 Paper Abstract


Paper ThA2.1

Heydari, Javad (Rensselaer Polytechnic Institute), Tajer, Ali (Rensselaer Polytechnic Institute), Poor, H. Vincent (Princeton University)

Quickest Detection of Gauss-Markov Random Fields

Scheduled for presentation during the Invited Session "Sequential and Quickest Change Detection" (ThA2), Thursday, October 1, 2015, 08:30−08:50, Solarium

53rd Annual Allerton Conference on Communication, Control, and Computing, Sept 29-Oct 2, 2015, Allerton Park and Retreat Center, Monticello, IL, USA

This information is tentative and subject to change. Compiled on December 5, 2021

Keywords Detection and Estimation, Statistical Signal Processing, Sensor Networks


The problem of quickest data-adaptive and sequential search for clusters in a Gauss-Markov random field is considered. In the existing literature, such search for clusters is often performed using fixed sample size and non-adaptive strategies. In order to accommodate large networks, in which data adaptivity leads to significant gains in detection quality and agility, in this paper sequential and data-adaptive detection strategies are proposed and are shown to enjoy asymptotic optimality. The quickest detection problem is abstracted by adopting an acyclic dependency graph to model the mutual effects of different random variables in the field and decision making rules are derived for general random fields and specialized for Gauss- Markov random fields. Performance evaluations demonstrate the gains of the data-adaptive schemes over existing techniques in terms of sampling complexity and error exponents.



All Content © PaperCept, Inc..

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2021 PaperCept, Inc.
Page generated 2021-12-05  08:46:33 PST  Terms of use