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dc.contributor.authorLi, Y.
dc.contributor.authorMazuelas, S. 
dc.contributor.authorShen, Y.
dc.date.accessioned2022-03-09T08:37:32Z
dc.date.available2022-03-09T08:37:32Z
dc.date.issued2021-12-30
dc.identifier.issn2155-7586
dc.identifier.urihttp://hdl.handle.net/20.500.11824/1445
dc.description.abstractLocalization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time consuming data collection. In this paper, we propose a semisupervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.en_US
dc.description.sponsorshipRamon y Cajal Grant RYC-2016-19383en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.rightsReconocimiento-NoComercial-CompartirIgual 3.0 Españaen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/es/en_US
dc.subjectVariational Bayes, Deep Learning, Semi- Supervised Learning, UWB Radio, ranging error mitigationen_US
dc.titleA Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveformen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.identifier.doi10.1109/MILCOM52596.2021.9653043en_US
dc.relation.projectIDES/2PE/PID2019-105058GA-I00en_US
dc.relation.projectIDEUS/ELKARTEKen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionen_US
dc.journal.titleMILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM)en_US


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Reconocimiento-NoComercial-CompartirIgual 3.0 España
Except where otherwise noted, this item's license is described as Reconocimiento-NoComercial-CompartirIgual 3.0 España