Mostrar el registro sencillo del ítem

dc.contributor.author
Wang, Wenxu  
dc.contributor.author
Marelli, Damian Edgardo  
dc.contributor.author
Fu, Minyue  
dc.date.available
2023-01-05T13:08:57Z  
dc.date.issued
2020-07  
dc.identifier.citation
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking; Molecular Diversity Preservation International; Sensors; 20; 10; 7-2020; 1-15  
dc.identifier.issn
1424-8220  
dc.identifier.uri
http://hdl.handle.net/11336/183504  
dc.description.abstract
Indoor positioning using Wi-Fi signals is an economic technique. Its drawback is that multipath propagation distorts these signals, leading to an inaccurate localization. An approach to improve the positioning accuracy consists of using fingerprints based on channel state information (CSI). Following this line, we propose a new positioning method which consists of three stages. In the first stage, which is run during initialization, we build a model for the fingerprints of the environment in which we do localization. This model permits obtaining a precise interpolation of fingerprints at positions where a fingerprint measurement is not available. In the second stage, we use this model to obtain a preliminary position estimate based only on the fingerprint measured at the receiver’s location. Finally, in the third stage, we combine this preliminary estimation with the dynamical model of the receiver’s motion to obtain the final estimation. We compare the localization accuracy of the proposed method with other rival methods in two scenarios, namely, when fingerprints used for localization are similar to those used for initialization, and when they differ due to alterations in the environment. Our experiments show that the proposed method outperforms its rivals in both scenarios.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Molecular Diversity Preservation International  
dc.rights
info:eu-repo/semantics/openAccess  
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/  
dc.subject
BAYESIAN TRACKING  
dc.subject
CSI  
dc.subject
FINGERPRINTING  
dc.subject
INDOOR LOCALIZATION  
dc.subject.classification
Control Automático y Robótica  
dc.subject.classification
Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información  
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS  
dc.title
Fingerprinting-based indoor localization using interpolated preprocessed csi phases and bayesian tracking  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2021-08-19T19:53:55Z  
dc.journal.volume
20  
dc.journal.number
10  
dc.journal.pagination
1-15  
dc.journal.pais
Suiza  
dc.journal.ciudad
Basel  
dc.description.fil
Fil: Wang, Wenxu. Guandong University Of Technology; China  
dc.description.fil
Fil: Marelli, Damian Edgardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas. Universidad Nacional de Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas; Argentina  
dc.description.fil
Fil: Fu, Minyue. Universidad de Newcastle; Australia  
dc.journal.title
Sensors  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3390/s20102854