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Author manuscript, published in "16th European Signal Processing Conference (EUSIPCO-2008), Lausanne: Switzerland (2008)" ALEASTSQUAREAPPROACHFORBIDIMENSIONALSOURCESEPARATION USINGHIGHERORDERSTATISTICSCRITERIA AmirA.Khan †,ValeriuVrabie ‡,JérômeI.Mars †,a

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Abstract

Theanomalydetectionbasedonprocessingofdistributed temperaturesensorsdataisanewresearchproblem.Theacquireddataishighlyinfluencedbytheresponseoftheground inwhichthesensorsareburied.Itthereforebecomesessentialtoremovetheinfluenceofthisresponse.Thisresponse, beingthemostcoherentfactorintheacquiredsignal,appears asthemostenergeticsourcevector. However,itsclassical estimationbySVDrunstheriskoftakingintoaccountenergeticphenomenalikeprecipitations.Weproposetocharacterizesuchphenomenausinghigherorderstatisticsthusgivingacriteriaofselectingonlythedatanotinfluencedbysuch phenomena. Anoverlappingwindowapproachthenallows estimationofcharacteristicgroundresponsesource. Moreover,thecorrespondinggroundresponsesubspaceisconstructedbyleastsquaresbasedunmixingapproachonthe characteristicsource.Thisavoidsalsothephysicallyunjustifiableorthogonalityconditionoftemporalvariationsofthe estimatedsourcesimposedbySVD

Year: 2008
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