Journal
IEEE ACCESS
Volume 8, Issue -, Pages 88951-88960Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2993576
Keywords
Wireless sensor networks; Wireless communication; Dictionaries; Sensors; Compressed sensing; Inference algorithms; Communication system security; Multi-target localization; device free localization; compressive sensing; variational EM algorithm; block sparsity
Categories
Funding
- National Natural Science Foundation of China [61871400]
- Natural Science Foundation of Jiangsu Province [BK20171401]
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In Device-Free Localization (DFL) schemes, most researches assume that the targets are stationary with fixed gestures and motions. However, objects always change their gestures in practice, undoubtedly influencing the localization accuracy. To solve the issue, we propose a new algorithm named Ges-DFL under the Compressive Sensing (CS) framework, which considers the time-varying target gestures in the DFL scheme. Firstly, leaning that the matrixes referring to different target gestures cannot be achieved, we transfer them to a fixed sensing matrix by the transferring method. Secondly, we build the localization scheme as an MMV recovery issue and exploit the block sparsity property of transferred location vectors to improve the localization accuracy. Thirdly, the new algorithm Ges-DFL scheme is designed under the framework of the variational Bayesian inference to reconstruct transferred location vectors. Simulations show that the proposed algorithm performs well both in localization accuracy and robustness.
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