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基于运动目标的红外图像末制导跟踪算法研究的中期报告 Abstract: Infraredimagingseekerisanimportantclassifierformodernwarfareanditsguidanceaccuracyplaysavitalroleintheeffectivenessofmissileweapons.Traditionaltrackingalgorithmsfacechallengesfromthechangingmovementpatternsoftargets.Thisreportdescribesaresearchprojectthataimstodevelopatargettrackingalgorithmthatcanaccuratelytracktargetsbasedontheirmovementgoals.Theprojectusesacombinationofinfraredimagingandmachinelearningalgorithms,andhasachievedsignificantprogressinthefirsthalf. Introduction: Infraredimagingseekerisawidelyusedtechnologyinmodernwarfare,anditsguidanceaccuracyplaysavitalroleintheeffectivenessofmissileweapons.Inordertoimprovetheeffectivenessofinfraredguidedmissiles,itisnecessarytodevelopatrackingalgorithmthatcanaccuratelytracktargetsbasedontheirmovementgoals. TraditionaltrackingalgorithmscommonlyadoptKalmanfilteringandparticlefilteringalgorithms,butthesealgorithmsfacechallengesfromthechangingmovementpatternsoftargets.Inordertoaddressthesechallenges,thisprojectproposesamethodfortargettrackingbasedonthemovementgoalsofthetarget. Method: Theprojectusesacombinationofinfraredimagingandmachinelearningalgorithms.Themachinelearningalgorithmisusedtoclassifythemovementgoalsofthetarget,andthetargettrackingalgorithmadjuststhetrackingstrategyaccordingtothetarget’smovementgoal. Theimageprocessingalgorithmisthecoreofthetargettrackingalgorithm.Thealgorithmfirstextractsthetargetfromtheinfraredimage,andthenobtainsthemovementinformationofthetargetbyanalyzingtheimagesequence.Themachinelearningalgorithmanalyzesthemovementinformationofthetargetandclassifiesthemovementgoalsofthetarget. Results: Theprojecthasachievedsignificantprogressinthefirsthalf.Theimageprocessingalgorithmhasachievedgoodresultsinextractingtargetsfrominfraredimagesandobtainingmovementinformation.Themachinelearningalgorithmhasachievedgoodresultsinclassifyingthemovementgoalsofthetarget. Conclusion: Basedontheachievementsofthefirsthalfoftheproject,theproposedmethodfortargettrackingbasedonthe