Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks

Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks

ID:40559733

大?。?.89 MB

頁(yè)數(shù):15頁(yè)

時(shí)間:2019-08-04

Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks_第1頁(yè)
Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks_第2頁(yè)
Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks_第3頁(yè)
Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks_第4頁(yè)
Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks_第5頁(yè)
資源描述:

《Adaptive Source Location Estimation Based on Compressed Sensing in Wireless Sensor Networks》由會(huì)員上傳分享,免費(fèi)在線閱讀,更多相關(guān)內(nèi)容在學(xué)術(shù)論文-天天文庫(kù)。

1、HindawiPublishingCorporationInternationalJournalofDistributedSensorNetworksVolume2012,ArticleID592471,15pagesdoi:10.1155/2012/592471ResearchArticleAdaptiveSourceLocationEstimationBasedonCompressedSensinginWirelessSensorNetworksLeiLiu,1,2Jin-SongChong,1Xiao-QingWang,1andWenHong11NationalKeyLaborator

2、yofScienceandTechnologyonMicrowaveImaging,InstituteofElectronics,ChineseAcademyofSciences,Beijing100190,China2GraduateUniversityofChineseAcademyofSciences,Beijing100049,ChinaCorrespondenceshouldbeaddressedtoLeiLiu,liulei2111@gmail.comReceived19March2011;Revised3July2011;Accepted14September2011Acade

3、micEditor:RajgopalKannanCopyright?2012LeiLiuetal.ThisisanopenaccessarticledistributedundertheCreativeCommonsAttributionLicense,whichpermitsunrestricteduse,distribution,andreproductioninanymedium,providedtheoriginalworkisproperlycited.Sourcelocalizationisanimportantprobleminwirelesssensornetworks(WS

4、Ns).Anexcitingstate-of-the-artalgorithmforthisproblemismaximumlikelihood(ML),whichhassu?cientspatialsamplesandconsumesmuchenergy.Inthispaper,ane?ectivemethodbasedoncompressedsensing(CS)isproposedformultiplesourcelocationsinreceivedsignalstrength-wirelesssensornetworks(RSS-WSNs).Thisalgorithmmodelsu

5、nknownmultiplesourcepositionsasasparsevectorbyconstructingredundantdictionaries.Thus,sourceparameters,suchassourcepositionsandenergy,canbeestimatedby1-normminimization.Tospeedupthealgorithm,ane?ectiveconstructionofmultiresolutiondictionaryisintroduced.Furthermore,toimprovethecapacityofresolvingtwo

6、sourcesthatareclosetoeachother,theadaptivedictionaryre?nementandtheoptimizationoftheredundantdictionaryarrangement(RDA)areutilized.ComparedtoMLmethods,suchasalternatingprojection,theCSalgorithmcanimprovetheresolutionofmultiplesourcesandreducespatialsamplesofWSNs.Thesimulationsresultsdemonstratethep

7、erformanceofthisalgorithm.1.IntroductionDOAandTDOAarenotverypracticalforlow-costandlow-powerWSNs.RSScane?ectivelyovercomethelimitationsWirelesssensornetworks(WSNs)[1,2]arewidelyappliedinofDOAandTDOA,thusinc

當(dāng)前文檔最多預(yù)覽五頁(yè),下載文檔查看全文

此文檔下載收益歸作者所有

當(dāng)前文檔最多預(yù)覽五頁(yè),下載文檔查看全文
溫馨提示:
1. 部分包含數(shù)學(xué)公式或PPT動(dòng)畫的文件,查看預(yù)覽時(shí)可能會(huì)顯示錯(cuò)亂或異常,文件下載后無(wú)此問(wèn)題,請(qǐng)放心下載。
2. 本文檔由用戶上傳,版權(quán)歸屬用戶,天天文庫(kù)負(fù)責(zé)整理代發(fā)布。如果您對(duì)本文檔版權(quán)有爭(zhēng)議請(qǐng)及時(shí)聯(lián)系客服。
3. 下載前請(qǐng)仔細(xì)閱讀文檔內(nèi)容,確認(rèn)文檔內(nèi)容符合您的需求后進(jìn)行下載,若出現(xiàn)內(nèi)容與標(biāo)題不符可向本站投訴處理。
4. 下載文檔時(shí)可能由于網(wǎng)絡(luò)波動(dòng)等原因無(wú)法下載或下載錯(cuò)誤,付費(fèi)完成后未能成功下載的用戶請(qǐng)聯(lián)系客服處理。