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dc.contributor.advisorMao, Shiwenen_US
dc.contributor.authorGao, Lingjunen_US
dc.date.accessioned2015-07-28T13:31:23Z
dc.date.available2015-07-28T13:31:23Z
dc.date.issued2015-07-28
dc.identifier.urihttp://hdl.handle.net/10415/4777
dc.description.abstractWith the fast growing demand of location-based services in indoor environments, indoor positioning based on fingerprinting has attracted a lot of interest due to its high accuracy. In this thesis, we present a novel deep learning based indoor fingerprinting system using Channel State Information (CSI), which is termed DeepFi. Based on three hypotheses on CSI, the DeepFi system architecture includes an off-line training phase and an on-line localization phase. In the off-line training phase, deep learning is utilized to train all the weights as fingerprints. Moreover, a greedy learning algorithm is used to train all the weights layer-by-layer to reduce complexity. In the on-line localization phase, we use a probabilistic method based on the radial basis function to obtain the estimated location. Experimental results are presented to confirm that DeepFi can effectively reduce location error compared with three existing methods in two representative indoor environments.en_US
dc.subjectElectrical Engineeringen_US
dc.titleChannel State Information Fingerprinting Based Indoor Localization: a Deep Learning Approachen_US
dc.typeMaster's Thesisen_US
dc.embargo.statusNOT_EMBARGOEDen_US
dc.contributor.committeeRoppel, Thaddeusen_US
dc.contributor.committeeTugnait, Jitendraen_US


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