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Article: The coordination generalized particle model-An evolutionary approach to multi-sensor fusion

TitleThe coordination generalized particle model-An evolutionary approach to multi-sensor fusion
Authors
KeywordsCoordination Generalized Particle Model (C-Gpm)
Dynamic Sensor Resource Allocation Problem
Evolutionary Algorithm
Multi-Sensor Fusion
Sensor Behavior
Sensor Coordination
Issue Date2008
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/inffus
Citation
Information Fusion, 2008, v. 9 n. 4, p. 450-464 How to Cite?
AbstractThe rising popularity of multi-source, multi-sensor networks supporting real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on the coordination generalized particle model (C-GPM) which is founded on the laws of physics. C-GPM treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed C-GPM approach can model the autonomy of as well as the social coordinations and interactive behaviors among sensors in a decentralized paradigm. Although the other existing evolutionary algorithms have their respective advantages, they may not be able to capture the entire dynamics inherent in the problem, especially those that are high-dimensional, highly nonlinear, and random. The C-GPM approach can overcome such limitations. We develop the C-GPM approach as a physics-based evolutionary approach that can describe such complex behaviors and dynamics of multiple sensors. © 2007 Elsevier B.V. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/152399
ISSN
2015 Impact Factor: 4.353
2015 SCImago Journal Rankings: 1.941
ISI Accession Number ID
Funding AgencyGrant Number
Hong Kong University200607176155
Funding Information:

We thank the editor and referees for their very clear and useful advice and comments. This work is supported by a Hong Kong University Small Project Funding (200607176155).

References

 

DC FieldValueLanguage
dc.contributor.authorFeng, Xen_US
dc.contributor.authorLau, FCMen_US
dc.contributor.authorShuai, Den_US
dc.date.accessioned2012-06-26T06:38:05Z-
dc.date.available2012-06-26T06:38:05Z-
dc.date.issued2008en_US
dc.identifier.citationInformation Fusion, 2008, v. 9 n. 4, p. 450-464en_US
dc.identifier.issn1566-2535en_US
dc.identifier.urihttp://hdl.handle.net/10722/152399-
dc.description.abstractThe rising popularity of multi-source, multi-sensor networks supporting real-life applications calls for an efficient and intelligent approach to information fusion. Traditional optimization techniques often fail to meet the demands. The evolutionary approach provides a valuable alternative due to its inherent parallel nature and its ability to deal with difficult problems. We present a new evolutionary approach based on the coordination generalized particle model (C-GPM) which is founded on the laws of physics. C-GPM treats sensors in the network as distributed intelligent agents with various degrees of autonomy. Existing approaches based on intelligent agents cannot completely answer the question of how their agents could coordinate their decisions in a complex environment. The proposed C-GPM approach can model the autonomy of as well as the social coordinations and interactive behaviors among sensors in a decentralized paradigm. Although the other existing evolutionary algorithms have their respective advantages, they may not be able to capture the entire dynamics inherent in the problem, especially those that are high-dimensional, highly nonlinear, and random. The C-GPM approach can overcome such limitations. We develop the C-GPM approach as a physics-based evolutionary approach that can describe such complex behaviors and dynamics of multiple sensors. © 2007 Elsevier B.V. All rights reserved.en_US
dc.languageengen_US
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/inffusen_US
dc.relation.ispartofInformation Fusionen_US
dc.rightsInformation Fusion. Copyright © Elsevier BV.-
dc.subjectCoordination Generalized Particle Model (C-Gpm)en_US
dc.subjectDynamic Sensor Resource Allocation Problemen_US
dc.subjectEvolutionary Algorithmen_US
dc.subjectMulti-Sensor Fusionen_US
dc.subjectSensor Behavioren_US
dc.subjectSensor Coordinationen_US
dc.titleThe coordination generalized particle model-An evolutionary approach to multi-sensor fusionen_US
dc.typeArticleen_US
dc.identifier.emailLau, FCM:fcmlau@cs.hku.hken_US
dc.identifier.authorityLau, FCM=rp00221en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1016/j.inffus.2007.01.001en_US
dc.identifier.scopuseid_2-s2.0-49749125581en_US
dc.identifier.hkuros129569-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-49749125581&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume9en_US
dc.identifier.issue4en_US
dc.identifier.spage450en_US
dc.identifier.epage464en_US
dc.identifier.isiWOS:000259437300004-
dc.publisher.placeNetherlandsen_US
dc.identifier.scopusauthoridFeng, X=55200149100en_US
dc.identifier.scopusauthoridLau, FCM=7102749723en_US
dc.identifier.scopusauthoridShuai, D=7003359432en_US

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