基于交互式多模型的机动目标跟踪算法研究的中期报告

基于交互式多模型的机动目标跟踪算法研究的中期报告摘要机动目标跟踪是目标识别、追踪和定位的一个重要研究方向。本文基于交互式多模型的机动目标跟踪算法，对该算法进行了研究，并给出了中期报告。首先介绍了机动目标跟踪的应用背景和研究意义，然后阐述了交互式多模型的机动目标跟踪算法原理及其流程，比较了该算法与传统的单一模型跟踪算法的优势和不足，同时对跟踪数据集和评价指标进行了介绍。接着，给出了目前算法实现情况，并对实验结果进行了分析和总结。最后，提出了下一步的研究计划和工作安排。关键词：机动目标跟踪；交互式多模型；跟踪算法；评价指标；实验结果AbstractManeuveringtargettrackingisanimportantresearchdirectionfortargetidentification,tracking,andpositioning.Basedontheinteractivemultiplemodelmaneuveringtargettrackingalgorithm,thispaperconductsresearchandgivesamidtermreport.Firstly,theapplicationbackgroundandresearchsignificanceofmaneuveringtargettrackingareintroduced.Then,theprincipleandprocessofinteractivemultiplemodelmaneuveringtargettrackingalgorithmareexpounded,andtheadvantagesanddisadvantagesofthisalgorithmarecomparedwithtraditionalsinglemodeltrackingalgorithm,atthesametime,thetrackingdatasetandevaluationindexareintroduced.Furthermore,thecurrentimplementationstatusofthealgorithmisgiven,andtheexperimentalresultsareanalyzedandsummarized.Finally,thenextresearchplanandworkarrangementareproposed.Keywords:maneuveringtargettracking;interactivemultiplemodel;trackingalgorithm;evaluationindex;experimentalresultsIntroductionManeuveringtargettrackingisachallengingtaskduetothecomplexmotionmodels,changingtargetcharacteristicsandenvironmentalconditions.Itiswidelyusedinmilitarysurveillance,remotesensing,trafficmonitoringandotherfields.Thetraditionalsinglemodeltrackingalgorithmhascertainlimitationsintrackingmaneuveringtargets.Inrecentyears,theinteractivemultiplemodel(IMM)algorithmhasbeenproposedandappliedtothemaneuveringtargettrackingproblem.TheIMMalgorithmcantracktargetswithdifferentmotionmodelsbycombiningmultipleKalmanfilters.Thecombinationweightsofdifferentmodelsareupdatedbythelikelihoodofthemeasurementinnovationvector.Inthisreport,weconductresearchontheIMMmaneuveringtargettrackingalgorithmandgiveamidtermreport.Firstly,weintroducetheapplicationbackgroundandresearchsignificanceofmaneuveringtargettracking.Secondly,weexpoundtheprincipleandprocessoftheIMMmaneuveringtargettrackingalgorithm,andcomparetheadvantagesanddisadvantagesofthisalgorithmwithtraditionalsinglemodeltrackingalgorithm.Then,weintroducethetrackingdatasetandevaluationindex.Furthermore,wegivethecurrentimplementationstatusofthealgorithmandanalyzetheexperimentalresults.Finally,weproposethenextresearchplanandworkarrangement.IMMmaneuveringtargettrackingalgorithmTheIMMalgorithmisafilteringandsmoothingalgorithmfortrackingmulti-modelnonlineardynamicalsystems.ThebasicideaoftheIMMalgorithmistocombinemultipleKalmanfilterstoformahybridfilter.Thehybridfiltercanswitchbetweendifferentmotionmodelstotrackamaneuveringtarget.Thecombinationweightsofdifferentmodelsareupdatedbasedonthelikelihoodofthemeasurementinnovationvector.TheIMMalgorithmcanbedividedintotwostages:predictionandupdate.Inthepredictionstage,thestateestimateandcovarianceofeachmodelarepredictedbasedonthecorrespondingmotionmodel.Intheupdatestage,themeasurementinnovationvectoranditscovariancearecalculatedforeachmodel.Thelikelihoodofthemeasurementinnovationvectoristhenusedtoupdatethecombinationweightsofdifferentmodels.Comparedwithtraditionalsinglemodeltrackingalgorithm,theIMMalgorithmhasthefollowingadvantages:1.TheIMMalgorithmcantracktargetswithdifferentmotionmodels,whichismorerobusttotargetmaneuvering.2.TheIMMalgorithmcanprovidemoreaccuratetrackingestimatesbycombiningmultipleestimatesfromdifferentmodels.However,theIMMalgorithmalsohassomedisadvantages:1.ThecomputationalcomplexityoftheIMMalgorithmishigherthanthatofthetraditionalsinglemodeltrackingalgorithm.2.TheIMMalgorithmrequiresaprioriknowledgeofthetargetmotionmodels,whichmayneedtobeupdatedinreal-timeaccordingtothetargetbehavior.TrackingdatasetandevaluationindexThedatasetusedinthisresearchisthepublicBenchmarkdataset,whichcontains50videosofvarioustypesoftargetsandenvironments.Theevaluationindexusedinthisresearchistheaverageintersection-over-union(IoU)ofthetrackingresultsandgroundtruth.CurrentimplementationstatusandexperimentalresultsTheIMMmaneuveringtargettrackingalgorithmhasbeenimplementedusingMatlab.TheexperimentalresultsshowthattheIMMalgorithmcanachievebettertrackingperformancethanthetraditionalsinglemodeltrackingalgorithminmostcases.However,thecomputationalcomplexityoftheIMMalgorithmishigherthanthatofthetraditionalsinglemodeltrackingalgorithm,whichlimitsitsreal-timetrackingcapability.NextresearchplanandworkarrangementThenextresearchplanistooptimizetheIMMalgorithmtoimproveitstrackingperformanceandreduceitscomputationalcomplexity.Theworkarrangementisasfollows:1.FurtheranalysisoftheadvantagesanddisadvantagesoftheIMMalgorithm,andcomparisonwithothertrackingalgorithms.2.OptimizationoftheIMMalgorithmtoreduceitscomputationalcomplexityandimproveitsreal-timetrackingcapability.3.ConductingmoreexperimentsonthetrackingdatasettoverifytheperformanceoftheoptimizedIMMalgorithm.ConclusionInthisreport,weconductresearchontheIMMmaneuveringtargettrackingalgorithmandgiveamidtermreport.TheIMMalgorithmcantracktargetswithdifferentmotionmodelsandprovidemore