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基于交互多模型的水下目标跟踪方法(英文).docx

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基于交互多模型的水下目标跟踪方法(英文) Underwatertargettrackingisanimportantresearchareainunderwaterroboticsandmarineengineering.Inrecentyears,therehasbeenagrowingdemandforaccurateandreliabletrackingofunderwatertargetsinvariousapplicationssuchasunderwaterexploration,surveillance,andmining.Oneofthemajorchallengesinunderwatertargettrackingisthecomplexityoftheunderwaterenvironment,whichpresentsmanyobstaclesanduncertainties,suchaswatercurrents,turbulence,andseafloortopography.Toaddressthesechallenges,researchershaveproposedvariousmethodsandtechniquesforunderwatertargettracking,includingsonar-basedmethods,vision-basedmethods,andhybridmethodsthatcombinemultiplesensingmodalities. Inthispaper,weproposeanovelunderwatertargettrackingmethodbasedoninteractivemultimodalsensing.Themethodintegratesmultipleunderwatersensingmodalities,includingsonar,vision,andhydrodynamicsensors,toachieverobustandaccuratetargettrackingincomplexunderwaterenvironments.Themethodaimstoleveragethestrengthsofeachsensingmodalityandcompensatefortheirweaknessesbyenablingeffectiveinteractionandfusionoftheirdata. Theproposedmethodconsistsofthreemainmodules:sensing,fusion,andtracking.Thesensingmodulecollectsdatafromthemultiplesensingmodalitiesandprocessesthemtoextractrelevantfeaturesandcuesfortargetlocalizationandtracking.Thefusionmoduleintegratestheinformationfromthedifferentsensingmodalitiesbyusingaprobabilisticdatafusionframeworkthatcombinestherelativeadvantagesofeachmodalityandprovidesamoreaccurateandcomprehensiverepresentationofthetarget'sstate.Finally,thetrackingmoduleusesthefuseddatatoestimatethetarget'strajectoryandupdateitspositionovertime. Theproposedmethodhasseveraladvantagescomparedtoexistingmethods.First,itenablesrobusttargettrackingeveninthepresenceofenvironmentaluncertainties,suchaswatercurrentsandturbulence,byusingmultiplesensingmodalitiesthatarelesssensitivetothesefactors.Second,itprovidesaricherandmorecompleterepresentationofthetarget'sstatebycombiningthecomplementaryinformationfromdifferentsensingmodalities,wh