邻近多目标场景下MIMO雷达检测数据处理算法研究

邻近多目标场景下MIMO雷达检测数据处理算法研究邻近多目标场景下MIMO雷达检测数据处理算法研究

摘要：

MIMO雷达是一种多输入多输出雷达系统，可以利用天线阵列快速获取地面目标信息，并提供较高的分辨率和检测性能。然而，在邻近多目标场景下，传统的MIMO雷达数据处理算法通常存在一些问题，如交叉干扰、信号混叠等。针对这些问题，本文提出了一种改进的邻近多目标场景下MIMO雷达数据处理算法。首先，使用半监督学习技术来减少交叉干扰的影响，并利用最小二乘法进行信号分离。其次，采用弱化总变差（TV）约束的方法来优化多目标信号的稀疏表示，并利用非局部均值（NLM）去除信号混叠。最后，利用仿真实验和实测数据进行算法测试，结果表明，该算法能够有效地提高邻近多目标场景下MIMO雷达的检测性能。

关键词：

MIMO雷达、多目标场景、交叉干扰、信号混叠、稀疏表示。

Abstract：

MIMOradarisamultiple-inputmultiple-outputradarsystemthatcanquicklyacquiregroundtargetinformationusingantennaarrayandprovidehighresolutionanddetectionperformance.However,intheadjacentmultiple-targetscenes,traditionalMIMOradardataprocessingalgorithmsusuallyhavesomeproblems,suchascrossinterferenceandsignalaliasing.Inordertosolvetheseproblems,thispaperproposesanimprovedMIMOradardataprocessingalgorithmforadjacentmultiple-targetscenes.Firstly,semi-supervisedlearningisusedtoreducetheimpactofcrossinterference,andtheleastsquaresmethodisusedtoseparatethesignals.Secondly,thetotalvariation(TV)constraintisweakenedtooptimizethesparserepresentationofmultipletargetsignals,andthenon-localmeans(NLM)methodisusedtoremovesignalaliasing.Finally,simulationexperimentsandactualmeasurementsareusedtotestthealgorithm,andtheresultsshowthattheproposedalgorithmcaneffectivelyimprovethedetectionperformanceofMIMOradarinadjacentmultiple-targetscenes.

Keywords:

MIMOradar,multiple-targetscenes,crossinterference,signalaliasing,sparserepresentationMultiple-targetscenesareacommonoccurrenceinmodernMIMOradarsystems.However,thesescenesposesignificantchallengestosignaldetectionduetothepresenceofcross-interferenceandsignalaliasing.Cross-interferenceariseswhentheechoesfromonetargetinterferewiththeechoesfromanother.Signalaliasing,ontheotherhand,iscausedbyoverlappingofthesignalsfromdifferenttargetsinthefrequencydomain,whichresultsinincorrectlocalizationofthetargets.

Toovercomethesechallenges,weproposeanewalgorithmbasedonsparserepresentationandnon-localmeans.Themainideabehindourapproachistoweakentheinterferencebetweenthetargetsignalsbyexploitingthesparsityofthescene.Specifically,weuseasparserepresentationmodeltoseparatethesignalsfromdifferenttargets,whichallowsustoreducethecross-interference.Thesparsitymodelassumesthatthesignalscanberepresentedasalinearcombinationofasmallnumberofbasisfunctionsordictionaryelements,whichiswell-suitedforMIMOradarsystems.

Inadditiontosparsity-basedprocessing,wealsousethenon-localmeans(NLM)methodtoaddresstheissueofsignalaliasing.TheNLMmethodisapopulardenoisingtechniquethatremovesnoisebyaveragingsimilarpatchesinanimage.Inourcase,weapplytheNLMmethodtotheMIMOradarsignalstoremovethealiasingcausedbyoverlappingofthesignalsfromdifferenttargets.

Toevaluatetheeffectivenessofourproposedalgorithm,weconductedsimulationexperimentsandactualmeasurements.TheresultsshowthatouralgorithmcaneffectivelyimprovethedetectionperformanceofMIMOradarinadjacentmultiple-targetscenes.Specifically,ouralgorithmachievesbettertargetlocalizationandhighersignal-to-interference-plus-noise-ratio(SINR)comparedtoexistingmethods.

Inconclusion,ourproposedalgorithmbasedonsparserepresentationandnon-localmeansisapromisingapproachtoaddressthechallengesofsignaldetectioninmultiple-targetscenesforMIMOradarsystems.OurapproachimprovestheperformanceandreliabilityofMIMOradarsystemsandhaspotentialapplicationsinareassuchassurveillance,mapping,andremotesensingOneareawhereourproposedalgorithmcouldbeparticularlyusefulisdisasterresponse.Insituationssuchasearthquakes,hurricanes,orfloods,traditionaldetectionmethodsmaybeinsufficientinidentifyingpotentialsurvivorsorlocatingburiedvictims.However,MIMOradarsystemsusingourproposedalgorithmcouldimprovethespeedandaccuracyofrescuesbyenablingbettertargetlocalizationandidentificationinthesescenarios.

Additionally,ouralgorithmcouldalsohaveapplicationsinautonomousvehiclesandrobotics.Byimprovingtargetdetectionandlocalizationcapabilities,autonomousvehiclescouldnavigatemoreaccurately,avoidingobstaclesandimprovingsafetyontheroad.Inthefieldofrobotics,ouralgorithmcouldenablebetterperceptionandinteractionwiththeenvironment,allowingrobotstoperformtaskssuchasmappingandexplorationmoreeffectively.

Overall,ourproposedalgorithmbasedonsparserepresentationandnon-localmeanshasthepotentialtogreatlyimprovetheperformanceandreliabilityofMIMOradarsystemsindetectingmultipletargetsincomplexenvironments.Itsapplicationsarewide-ranging,fromdisasterresponseandsurveillancetoautonomousvehiclesandrobotics.Furtherresearchanddevelopmentinthisareacouldleadtosignificantadvancementsintargetdetectionandlocalization,benefitingarangeofindustriesandsocietalneedsSparserepresentationandnon-localmeansalgorithmshavebecomeapromisingsolutionforthedetectionandlocalizationofmultipletargetsincomplexenvironmentsusingMIMOradarsystems.Thistechniquehasshownitspotentialtoimprovethereliabilityandaccuracyoftargetdetectioninavarietyofapplicationsspanningacrossdifferentindustries.

Oneofthemajorbenefitsofthesparserepresentation-basedalgorithmisitsabilitytocapitalizeontheinherentsparsityofthesignaltoaccuratelyidentifyweakandcloselyspacedtargets.Thispropertybecomesparticularlyusefulinenvironmentswheretargetsarecloselylocated,andconventionaltechniquesmaystruggletoresolvethem.Thealgorithmachievesthisbyrepresentingthesignalasalinearcombinationoffewatomsfromapre-defineddictionary.Thesparsecoefficientsarethenestimatedusinganoptimizationmethod,suchasBasisPursuitorOrthogonalMatchingPursuit.ThismethodhasbeenshowntoimprovethedetectionandlocalizationperformanceofMIMOradarsystemsinvariousstudies.

Thenon-localmeansalgorithm,ontheotherhand,exploitstheredundancyofinformationinthereceivedsignaltosuppressnoiseandenhancethesignal-to-noiseratio(SNR).Theapproachreliesontheideathattargetsthatarespatiallyclosewillhavesimilarscatteringcharacteristics.Byexploitingthissimilarity,onecanestimatethesignalatagivenlocationbyaveragingthesignalatotherlocationswithinaspecificrange.Thisaveragingprocesshelpstosuppressrandomnoiseandpreservesignalfeatures,resultinginanimprovementintheSNR.

Thecombinationofthesetwoalgorithmshasshowntoyieldexceptionalresultsindetectingmultipletargetswithhighaccuracy,eveninenvironmentswithahighlevelofnoiseandclutter.Moreover,thealgorithmsrequirelesscomputationtimethantraditionalmethods,whichcanbeadvantageousinreal-timeapplications.Thismakesthemsuitableforawiderangeofapplications,includingsurveillance,disasterresponse,autonomousvehicles,androbotics.

Fordisasterresponseandsurveillanceapplications,theuseofradarsystemsindetectingandlocalizingtargetscanprovecritical.Theabilitytoaccuratelydetectandlocatepeopleorobjectsinadisaster-strickenareacanprovidevaluableinformationtotherescueteams,facilitatingthesearchandrescueprocess.Similarly,insurveillanceapplications,theuseofradarsystemscanhelpindetectingandtrackingintrudersinrestrictedareas,enhancingsecurity.Thesparserepresentationandnon-localmeansalgorithmscanimprovetheperformanceoftheradarsystemsusedintheseapplications,leadingtobetterresults.

Intheautonomousvehicleindustry,robusttargetdetectionandtrackingarecrucialforsafenavigation.Theuseofradarsystemsinautonomousvehiclesforobjectdetectionandlocalizationhasrapidlyincreasedinrecentyears.Thecombinationofthesparserepresentationandnon-localmeansalgorithmscanfurtherenhancetheperformanceofthesesystems,improvingtheirreliabilityandaccuracyindetectingandtrackingobjects,thusleadingtosaferautonomousnavigation.

Intheroboticsindustry,theuseofradarsystemscanenablerobotstooperateincomplexenvironmentswithvaryinglightingconditions.Thecombinationofthesparserepresentationandnon-localmeansalgorithmscanimprovetheperformanceoftheradarsystems,makingtherobotsbetterequippedtodetectandlocalizeobjects,leadingtobetterdecision-makingcapabilities.

Inconclusion,thesparserepresentationandnon-localmeansalgorithmshaveshownthepotentialtogreatlyimprov