Asymptotically optimal search of unknown anomalies

Bar Hemo, Kobi Cohen, Qing Zhao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

The problem of detecting an anomalous process over multiple processes is considered. We consider a composite hypothesis case, in which the measurements drawn when observing a process follow a common distribution parameterized by an unknown parameter (vector). The unknown parameter belongs to one of two disjoint parameter spaces, depending on whether the process is normal or abnormal. The objective is a sequential search strategy that minimizes the expected detection time subject to an error probability constraint. We develop a deterministic search policy to solve the problem and prove its asymptotic optimality (as the error probability approaches zero) when the parameter under the null hypothesis is known. We further provide an explicit upper bound on the error probability for the finite sample regime.

Original languageEnglish
Title of host publication2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages75-80
Number of pages6
ISBN (Electronic)9781509058440
DOIs
StatePublished - 23 Mar 2017
Event2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016 - Limassol, Cyprus
Duration: 12 Dec 201614 Dec 2016

Publication series

Name2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016

Conference

Conference2016 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2016
Country/TerritoryCyprus
CityLimassol
Period12/12/1614/12/16

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing

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