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dc.contributor.authorLE MOAL, G
dc.contributor.authorMORARU, George
dc.contributor.authorVERON, Philippe
dc.contributor.authorDOUILLY, M
dc.contributor.authorRABATE, P
dc.date.accessioned2015
dc.date.available2015
dc.date.issued2011
dc.date.submitted2015
dc.identifier.urihttp://hdl.handle.net/10985/9809
dc.description.abstractThis paper addresses the problem of robust detection of signal singularity in hostile environments using multisensor data fusion. Measurement uncertainty is usually treated in a probabilistic way, assuming lack of knowledge is totally due to random effects. However, this approach fails when other effects, such as sensor failure, are involved. In order to improve the robustness of singularity detection, an evidence theory based approach is proposed for both modeling (data alignment) and merging (data fusion) information coming from multiple redundant sensors. Whereas the fusion step is done classically, the proposed method for data alignment has been designed to improve singularity detection performances in multisensor cases. Several case studies have been designed to suit real life situations. Results provided by both probabilistic and evidential approaches are compared. Evidential methods show better behavior facing sensors dysfunction and the proposed method takes fully advantage of component redundancy.
dc.language.isoen
dc.publisherProceedings of the 14th International Conference on Information Fusion - FUSION
dc.rightsPost-print
dc.titleMultisensor data fusion and belief functions for robust singularity detection in signals
dc.typdocCommunications avec actes
dc.localisationCentre de Aix en Provence
dc.subject.halInformatique: Ingénierie assistée par ordinateur
ensam.audienceInternationale
ensam.conference.titleProceedings of the 14th International Conference on Information Fusion - FUSION
ensam.conference.date2011
ensam.countryEtats-Unis
ensam.title.proceedingProceedings of the 14th International Conference on Information Fusion - FUSION
ensam.pageNA
ensam.cityChicago


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