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| Compressed sensing radar imaging of polar mesospheric summer echoes using tracking and MIMO approaches (CS-PMSE-MIMO) | |
| 项目编号 | http://gepris.dfg.de/gepris/projekt/403837627 |
| sProfessor Dr. JorgeChau | |
| 项目主持机构 | Leibniz-Institut für Atmosphärenphysik an der Universität Rostock (IAP) |
| 开始日期 | 2018 |
| 英文摘要 | Thebasicphysicsbehindtheexistenceofpolarmesosphericsummerechoes(PMSE)isnowadayswellunderstood,whereatmosphericturbulence,chargediceparticlesandelectronsplaysignificantroles.Giventhisbasicunderstanding,PMSEarebeingusedastracerstostudythecomplicatedatmosphericdynamicsatpolarmesosphericaltitudes.PMSEobservationswithtypicalatmosphericradarsaredifficulttointerpret,sincetemporalandspatialfeaturescannotbeseparated.Inordertoresolvethesetemporalandspatialambiguities,atmosphericradarimaging(ARI)hasbeenappliedwithdifferentdegreesofsuccess,duetothesystemsused(beamwidths,limitednumberofreceivers,etc.),andduetothenatureoftheechoes.Ingeneraltheechoespresentrelativelylongcorrelationstimes(fewhundredsofmillisecondstoseconds)whiletheyarehorizontallydrifting.Suchdriftingdoesnotallowustoreducetheuncertaintiesontheobtainedspatialcorrelationsusedintraditionalmethods.Usually,beamformingtypealgorithms,someofthemincludingsometypeofregularization,areusedforimageformation.Unfortunately,thisleadstoartifactsintheimage.Apossiblesolutiontothischallengeistheexploitationofaprioriknowledgeabouttheimage.Typically,theimageissparseandonlychangesslowlyovertime.Theapplicationofcompressedsensing(CS)techniquesinARIhasbeenproposedbyotherresearchgroupsbutneedsfurtherinvestigationandimplementation.WehaverecentlyappliedcoherentMIMOtechniquesinARItostudyionosphericirregularities.ThiswasthefirsttimeMIMOwasusedinatmosphericradars.CombiningMIMOwithCSrisesmanychallengingresearchquestionsasthesensingmatrixishighlystructured.Furthermore,thecombinationofCSandtrackingopensanewfieldofresearchinARI.Firsttheoreticalchallengesandopportunitiesarisefromthefactthatthenumberofmeasurementsmaynotbelargeenoughsothatexistingresultsandalgorithmsforlargeproblemscannotbeapplied.Specialchallengesarisefromthefactthatwehavetocharacterizethesparsity,i.e.,thedomaininwhichitholds,andthetimedynamicswithouthavingareliablereference.Apossiblesolutiontothisproblemmightbetheuseofrecoveryandtrackingalgorithmswhichdonotfocusonmakingabesteffortinimagereconstructionalonebutalsoyieldsomeinformationonthetrustworthinessoftheresult.Besidessimulations,wewillexploitexistingradarexperimentstocreatephysicallymotivatedmodelsforthesparsityandthetimedynamics,andconductnewexperimentstotestandimproveourproposedmethods.TheinclusionofMIMO,besideshelpingintheinversion,mightservealsoastestscenariotoevaluatetheperformanceoftheproposedmethodsinsystemsnotabletouseMIMO. |
| 学科分类 | 1201 - 电子学与信息系统;12 - 信息科学 |
| 资助机构 | DE-DFG |
| 项目类型 | Priority Programmes |
| 国家 | DE |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/69379 |
| 推荐引用方式 GB/T 7714 | sProfessor Dr. JorgeChau.Compressed sensing radar imaging of polar mesospheric summer echoes using tracking and MIMO approaches (CS-PMSE-MIMO).2018. |
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