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dc.contributor.author
Wang, Wenxu  
dc.contributor.author
Marelli, Damian Edgardo  
dc.contributor.author
Fu, Minyue  
dc.date.available
2023-01-05T13:08:20Z  
dc.date.issued
2020-10  
dc.identifier.citation
Wang, Wenxu; Marelli, Damian Edgardo; Fu, Minyue; Multiple-Vehicle Localization Using Maximum Likelihood Kalman Filtering and Ultra-Wideband Signals; Institute of Electrical and Electronics Engineers; IEEE Sensors Journal; 21; 4; 10-2020; 4949-4956  
dc.identifier.issn
1530-437X  
dc.identifier.uri
http://hdl.handle.net/11336/183501  
dc.description.abstract
In this article we study the problem of localizing a fleet of vehicles in an indoor environment using ultra-wideband (UWB) signals. This is typically done by placing a number of UWB anchors with respect to which vehicles measure their distances. The localization performance is usually poor in the vertical axis, due to the fact that anchors are often placed at similar heights. To improve accuracy, we study the use of inter-vehicle distance measurements. These measurements introduce a technical challenge, as this requires the joint estimation of positions of all vehicles, and currently available methods become numerically complex. To go around this, we use a recently proposed technique called maximum likelihood Kalman filtering (MLKF). We present experiments using real data, showing how the addition of inter-vehicle measurements improves the localization accuracy by about 60%. Experiments also show that the MLKF achieves a localization error similar to the best among available methods, while requiring only about 20% of computational time.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Institute of Electrical and Electronics Engineers  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/  
dc.subject
INDOOR LOCALIZATION  
dc.subject
INTER-VEHICLE MEASUREMENT  
dc.subject
MAXIMUM LIKELIHOOD KALMAN FILTER  
dc.subject
UAV  
dc.subject
UWB  
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
Multiple-Vehicle Localization Using Maximum Likelihood Kalman Filtering and Ultra-Wideband Signals  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2021-08-19T19:53:48Z  
dc.journal.volume
21  
dc.journal.number
4  
dc.journal.pagination
4949-4956  
dc.journal.pais
Estados Unidos  
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
IEEE Sensors Journal  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/JSEN.2020.3031377