Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/589744
Title: Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm
Authors: Dongping Yu;Yan Guo;Ning Li;DAgang Fang
subject: shadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensing
Year: 2016
Publisher: IEEE
Abstract: Device-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.
Description: 
URI: http://localhost/handle/Hannan/168536
http://localhost/handle/Hannan/589744
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

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Title: Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm
Authors: Dongping Yu;Yan Guo;Ning Li;DAgang Fang
subject: shadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensing
Year: 2016
Publisher: IEEE
Abstract: Device-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.
Description: 
URI: http://localhost/handle/Hannan/168536
http://localhost/handle/Hannan/589744
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7815398.pdf4.8 MBAdobe PDFThumbnail
Preview File
Title: Dictionary Refinement for Compressive Sensing Based Device-Free Localization via the Variational EM Algorithm
Authors: Dongping Yu;Yan Guo;Ning Li;DAgang Fang
subject: shadowing effect|device-free localization|Wireless sensor networks|variational EM algorithm|dictionary refinement|compressive sensing
Year: 2016
Publisher: IEEE
Abstract: Device-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.
Description: 
URI: http://localhost/handle/Hannan/168536
http://localhost/handle/Hannan/589744
ISSN: 2169-3536
volume: 4
Appears in Collections:2016

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