Kalman濾波在高動態(tài)GNSS接收機(jī)中的研究與設(shè)計(jì)的中期報告_第1頁
Kalman濾波在高動態(tài)GNSS接收機(jī)中的研究與設(shè)計(jì)的中期報告_第2頁
Kalman濾波在高動態(tài)GNSS接收機(jī)中的研究與設(shè)計(jì)的中期報告_第3頁
全文預(yù)覽已結(jié)束

下載本文檔

版權(quán)說明：本文檔由用戶提供并上傳，收益歸屬內(nèi)容提供方，若內(nèi)容存在侵權(quán)，請進(jìn)行舉報或認(rèn)領(lǐng)

文檔簡介

Kalman濾波在高動態(tài)GNSS接收機(jī)中的研究與設(shè)計(jì)的中期報告IntroductionKalmanfilterisawidelyusedtechniqueforprocessingGNSSmeasurementsandimprovingtheaccuracyofGNSSpositioning.Inthismidtermreport,wediscusstheongoingresearchprojectabouttheapplicationofKalmanfilterinhigh-dynamicGNSSreceivers.Thereportcoversthefollowingaspects:1.Introductiontohigh-dynamicGNSSreceivers2.OverviewofKalmanfilter3.Kalmanfilter-basedalgorithmsforhigh-dynamicGNSSreceivers4.Simulationresultsandanalysis5.ConclusionsandfutureworkHigh-dynamicGNSSReceiversHigh-dynamicGNSSreceiversrefertodevicesthataredesignedtoprocessGNSSsignalsinhigh-dynamic,high-speed,andhigh-accelerationenvironments.Thesereceiversarecommonlyusedinapplicationssuchasaviation,landandmaritimenavigation,missileandrocketguidance,defense,andsportstracking.High-dynamicGNSSreceiversencounterseveralchallengesthatimpacttheaccuracyandreliabilityofGNSSmeasurements,suchas:1.Signalblockageandinterferencecausedbybuildings,trees,bridges,andotherobstacles.2.Highlevelsofnoiseandmultipathcausedbyreflectionsfromsurfacessuchasbuildings,water,andtheground.3.DynamicandvariableionosphericandatmosphericconditionsthatimpactthepropagationofGNSSsignals.4.Highreceiverdynamicscausedbythemovementofthereceiver,whichcanresultinrapidchangesinthereceivedsignal.Therefore,itisessentialtodevelopalgorithmsandtechniquesthatcanmitigatetheeffectofthesechallengesandimprovetheaccuracyofGNSSpositioninginhigh-dynamicenvironments.OverviewofKalmanFilterKalmanfilterisamathematicaltechniquethatusesarecursivealgorithmtoestimatethestateofasystemfromaseriesofnoisymeasurements.Thefilterestimatesthefuturestateofthesystembycombiningthecurrentstateestimatewiththenewmeasurementinformationwhiletakingintoaccountthemeasurementerrorsandthedynamicsofthesystem.Kalmanfilterusestwomaincomponents,thestatespacemodel,andthemeasurementmodel.Thestatespacemodelisusedtodescribethedynamicsofthesystem,whilethemeasurementmodelisusedtorelatethemeasurementstothestatevariables.Thefilterusesthesemodelstoestimatethestateofthesystembasedonasequenceofmeasurementsovertime.Theresultisafilteredestimateoftheunderlyingsystemstate,whichislessaffectedbynoiseanderrorsthantheindividualmeasurements.KalmanFilter-basedAlgorithmsforHigh-DynamicGNSSReceiversSeveralKalmanfilter-basedalgorithmshavebeenproposedintheliteraturetoimprovetheaccuracyandreliabilityofGNSSpositioninginhigh-dynamicenvironments.Thesealgorithmscanbeclassifiedintotwomaincategories:batchprocessingandreal-timeprocessing.BatchProcessingAlgorithmsBatchprocessingalgorithmsinvolveprocessingabatchofGNSSmeasurementsafterthedatahasbeencollected.Inthismethod,themeasurementsarecollectedoveraperiodoftimeandthenprocessedofflinetoestimatethereceiver'sposition.Themainadvantageofbatchprocessingalgorithmsisthattheyallowmoreflexibilityinthefilteringtechniqueandcanhandlelargeamountsofdata.OnepopularbatchprocessingalgorithmistheExtendedKalmanFilter(EKF).EKFisanextensionoftheKalmanfilterthatisusedwhenthestatetransitionandmeasurementmodelsarenonlinear.EKFapproximatesthenonlinearmodelsbylinearizingthemaroundthecurrentstateestimate.ThislinearapproximationallowstheuseofthestandardKalmanfilteralgorithmforstateestimation.Real-TimeProcessingAlgorithmsReal-timeprocessingalgorithmsaredesignedtoprovidereal-timeestimatesofthereceiver'sposition,velocity,andotherstatevariables.Thesealgorithmsusuallyoperateinarecursivemanner,wherethecurrentstateestimateisupdatedcontinuouslyasnewmeasurementsarrive.Onepopularreal-timeprocessingalgorithmistheUnscentedKalmanFilter(UKF).UKFisanon-linearextensionoftheKalmanfilterthatdoesnotrequirelinearapproximationsofthestatetransitionandmeasurementmodels.UKFusesasetofcarefullychosensamplepoints(calledsigmapoints)topropagatethestatedistributionthroughthenonlinearfunctions.SimulationResultsandAnalysisWeconductedasimulationstudytocomparetheperformanceofdifferentKalmanfilter-basedalgorithmsforhigh-dynamicGNSSreceivers.Thesimulationinvolvedgeneratingarandomwalktrajectoryofahigh-speedreceiverandsimulatingGNSSobservationsbasedonthetrajectory.Thesimulationparameterswerechosentomimicahigh-dynamicenvironment,withahighlevelofnoise,multipath,anddynamiceffects.WecomparedtheperformanceofEKFandUKFalgorithmsfordifferentlevelsofnoiseandmultipath.TheresultsshowedthatbothalgorithmscansignificantlyimprovetheaccuracyofGNSSpositioninginhigh-dynamicenvironments,withUKFoutperformingEKFinmostcases.ConclusionsandFutureWorkInconclusion,Kalmanfilter-basedalgorithmsareeffectiveforimprovingtheaccuracyandreliabilityofGNSSpositioninginhigh-dynamicenvironments.WehavediscussedtheapplicationofKalmanfilteralgorithmsinhigh-dynamicGNSSreceiversandcomparedtheperformanceofEKFandUKFalgorithmsusingsimulationstudies.Futureworkincludesimplementingthedevelopedalgorithmsinareal-timesystemandtestingtheminanactualhigh-dynamicenvi

溫馨提示

  • 1. 本站所有資源如無特殊說明，都需要本地電腦安裝OFFICE2007和PDF閱讀器。圖紙軟件為CAD,CAXA,PROE,UG,SolidWorks等.壓縮文件請下載最新的WinRAR軟件解壓。
  • 2. 本站的文檔不包含任何第三方提供的附件圖紙等，如果需要附件，請聯(lián)系上傳者。文件的所有權(quán)益歸上傳用戶所有。
  • 3. 本站RAR壓縮包中若帶圖紙，網(wǎng)頁內(nèi)容里面會有圖紙預(yù)覽，若沒有圖紙預(yù)覽就沒有圖紙。
  • 4. 未經(jīng)權(quán)益所有人同意不得將文件中的內(nèi)容挪作商業(yè)或盈利用途。
  • 5. 人人文庫網(wǎng)僅提供信息存儲空間，僅對用戶上傳內(nèi)容的表現(xiàn)方式做保護(hù)處理，對用戶上傳分享的文檔內(nèi)容本身不做任何修改或編輯，并不能對任何下載內(nèi)容負(fù)責(zé)。
  • 6. 下載文件中如有侵權(quán)或不適當(dāng)內(nèi)容，請與我們聯(lián)系，我們立即糾正。
  • 7. 本站不保證下載資源的準(zhǔn)確性、安全性和完整性, 同時也不承擔(dān)用戶因使用這些下載資源對自己和他人造成任何形式的傷害或損失。

評論

0/150

提交評論