GLRT Based Anomaly Detection for Sensor Network Monitoring

Handle URI:
http://hdl.handle.net/10754/595932
Title:
GLRT Based Anomaly Detection for Sensor Network Monitoring
Authors:
Harrou, Fouzi; Sun, Ying ( 0000-0001-6703-4270 )
Abstract:
Proper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2015 IEEE Symposium Series on Computational Intelligence
Conference/Event name:
2015 IEEE Symposium Series on Computational Intelligence
Issue Date:
7-Dec-2015
DOI:
10.1109/SSCI.2015.66
Type:
Conference Paper
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7376639
Appears in Collections:
Conference Papers; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorSun, Yingen
dc.date.accessioned2016-02-09T13:24:10Zen
dc.date.available2016-02-09T13:24:10Zen
dc.date.issued2015-12-07en
dc.identifier.doi10.1109/SSCI.2015.66en
dc.identifier.urihttp://hdl.handle.net/10754/595932en
dc.description.abstractProper operation of antenna arrays requires continuously monitoring their performances. When a fault occurs in an antenna array, the radiation pattern changes and can significantly deviate from the desired design performance specifications. In this paper, the problem of fault detection in linear antenna arrays is addressed within a statistical framework. Specifically, a statistical fault detection method based on the generalized likelihood ratio (GLR) principle is utilized for detecting potential faults in linear antenna arrays. The proposed method relies on detecting deviations in the radiation pattern of the monitored array with respect to a reference (fault-free) one. To assess the abilities of the GLR based fault detection method, three case studies involving different types of faults have been performed. The simulation results clearly illustrate the effectiveness of the GLR-based fault detection method in monitoring the performance of linear antenna arrays.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7376639en
dc.rights(c) 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en
dc.titleGLRT Based Anomaly Detection for Sensor Network Monitoringen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journal2015 IEEE Symposium Series on Computational Intelligenceen
dc.conference.date7-10 Dec. 2015en
dc.conference.name2015 IEEE Symposium Series on Computational Intelligenceen
dc.conference.locationCape Townen
dc.eprint.versionPost-printen
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.