Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/589744
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dc.contributor.authorDongping Yuen_US
dc.contributor.authorYan Guoen_US
dc.contributor.authorNing Lien_US
dc.contributor.authorDAgang Fangen_US
dc.date.accessioned2020-05-20T08:41:03Z-
dc.date.available2020-05-20T08:41:03Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2017.2649540en_US
dc.identifier.urihttp://localhost/handle/Hannan/168536en_US
dc.identifier.urihttp://localhost/handle/Hannan/589744-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDevice-free localization (DFL) plays an increasingly important role in many security and military applications. It can realize localization without the requirement of equipping targets with any devices for signal transmitting or receiving. To reduce the number of measurements in DFL, compressive sensing (CS) theory has been applied. By exploiting the sparse nature of location finding problem, the target location vector can be estimated from a few measurements. However, in changing environments, measurements may diverge from those in a fixed dictionary (sensing matrix), and the mismatches between the dictionary and runtime measurements can significantly deteriorate the localization performance of CS-based DFL methods. To address this, we propose a novel dictionary refinement-based DFL method. It adopts the saddle surface model to characterize the shadowing effects caused by targets and parameterizes the dictionary with the shadowing rate of each link as the underlying parameters. Then, the variational expectation-maximization algorithm is adopted to realize joint localization and dictionary refinement. Simulation results show that the proposed approach achieves higher accuracy and robustness compared with the state-of-the-art fixed dictionary DFL methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7815398.pdfen_US
dc.subjectshadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensingen_US
dc.titleDictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithmen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

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Full metadata record
DC FieldValueLanguage
dc.contributor.authorDongping Yuen_US
dc.contributor.authorYan Guoen_US
dc.contributor.authorNing Lien_US
dc.contributor.authorDAgang Fangen_US
dc.date.accessioned2020-05-20T08:41:03Z-
dc.date.available2020-05-20T08:41:03Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2017.2649540en_US
dc.identifier.urihttp://localhost/handle/Hannan/168536en_US
dc.identifier.urihttp://localhost/handle/Hannan/589744-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDevice-free localization (DFL) plays an increasingly important role in many security and military applications. It can realize localization without the requirement of equipping targets with any devices for signal transmitting or receiving. To reduce the number of measurements in DFL, compressive sensing (CS) theory has been applied. By exploiting the sparse nature of location finding problem, the target location vector can be estimated from a few measurements. However, in changing environments, measurements may diverge from those in a fixed dictionary (sensing matrix), and the mismatches between the dictionary and runtime measurements can significantly deteriorate the localization performance of CS-based DFL methods. To address this, we propose a novel dictionary refinement-based DFL method. It adopts the saddle surface model to characterize the shadowing effects caused by targets and parameterizes the dictionary with the shadowing rate of each link as the underlying parameters. Then, the variational expectation-maximization algorithm is adopted to realize joint localization and dictionary refinement. Simulation results show that the proposed approach achieves higher accuracy and robustness compared with the state-of-the-art fixed dictionary DFL methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7815398.pdfen_US
dc.subjectshadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensingen_US
dc.titleDictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithmen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7815398.pdf4.8 MBAdobe PDFThumbnail
Preview File
Full metadata record
DC FieldValueLanguage
dc.contributor.authorDongping Yuen_US
dc.contributor.authorYan Guoen_US
dc.contributor.authorNing Lien_US
dc.contributor.authorDAgang Fangen_US
dc.date.accessioned2020-05-20T08:41:03Z-
dc.date.available2020-05-20T08:41:03Z-
dc.date.issued2016en_US
dc.identifier.issn2169-3536en_US
dc.identifier.other10.1109/ACCESS.2017.2649540en_US
dc.identifier.urihttp://localhost/handle/Hannan/168536en_US
dc.identifier.urihttp://localhost/handle/Hannan/589744-
dc.descriptionen_US
dc.descriptionen_US
dc.description.abstractDevice-free localization (DFL) plays an increasingly important role in many security and military applications. It can realize localization without the requirement of equipping targets with any devices for signal transmitting or receiving. To reduce the number of measurements in DFL, compressive sensing (CS) theory has been applied. By exploiting the sparse nature of location finding problem, the target location vector can be estimated from a few measurements. However, in changing environments, measurements may diverge from those in a fixed dictionary (sensing matrix), and the mismatches between the dictionary and runtime measurements can significantly deteriorate the localization performance of CS-based DFL methods. To address this, we propose a novel dictionary refinement-based DFL method. It adopts the saddle surface model to characterize the shadowing effects caused by targets and parameterizes the dictionary with the shadowing rate of each link as the underlying parameters. Then, the variational expectation-maximization algorithm is adopted to realize joint localization and dictionary refinement. Simulation results show that the proposed approach achieves higher accuracy and robustness compared with the state-of-the-art fixed dictionary DFL methods.en_US
dc.publisherIEEEen_US
dc.relation.haspart7815398.pdfen_US
dc.subjectshadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensingen_US
dc.titleDictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithmen_US
dc.typeArticleen_US
dc.journal.volume4en_US
dc.journal.titleIEEE Accessen_US
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7815398.pdf4.8 MBAdobe PDFThumbnail
Preview File