Spatial statistical analysis for the design of indoor particle-filter-based localization mechanisms

dc.contributorluis.orozco@uclm.es
dc.creatorMartínez Del Horno, Miguel
dc.creatorGarcía Varea, Ismael
dc.creatorGarcía Varea, Ismael
dc.creatorMartínez Gómez, Jesus
dc.creatorMartínez Gómez, Jesus
dc.creatorMartínez Gómez, Jesus
dc.creatorMartínez Del Horno, Miguel
dc.creatorCastillo Cara, Manuel
dc.creatorBrea Luján, Víctor Manuel
dc.creatorOrozco Barbosa, Luis
dc.creatorGarcía Varea, Ismael
dc.date2017-06-14T16:47:18Z
dc.date2017-06-14T16:47:18Z
dc.date2016-08
dc.date.accessioned2019-04-24T22:39:25Z
dc.date.available2019-04-24T22:39:25Z
dc.identifier15501329
dc.identifierhttp://cybertesis.uni.edu.pe/handle/uni/3267
dc.identifierInternational Journal of Distributed Sensor Networks
dc.identifier10.1177/1550147716661953
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/2345029
dc.descriptionThe accurate localization of end-users and resources is seen as one of the main pillars toward the successful implementation of context-based applications. While current outdoor localization mechanisms fulfill most application requirements, the design of accurate indoor localization mechanisms is still an open issue. Most research efforts are focusing on the design of mechanisms making use of the receiver signal strength indications generated by WLAN (wireless local area network) devices. However, the accuracy and robustness of such mechanisms can be severely compromised due to the random and unpredictable nature of radio channels. In this article, we develop a methodology incorporating various algorithms capable of coping with the unpredictable nature of radio channels. Following a holistic approach, we start by identifying the wireless equipment parameter setting, better meeting the implementation requirements of a robust indoor localization mechanism. We then make use of RANdom SAmple Consensus paradigm: a robust model-fitting mechanism capable of smoothing the data captured during the space survey. Using an experimental setup, we evaluate the benefits of integrating the floor plan and an ordinary Kriging interpolation algorithm in the estimation process. Our main findings show that our proposal can greatly improve the quality of the information to be used in the development of particle-filter-based indoor localization mechanisms.
dc.formatapplication/pdf
dc.languageeng
dc.publisherHindawi Publishing Corporation
dc.relationhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84984889419&doi=10.1177%2f1550147716661953&partnerID=40&md5=ecea0769133f70c3dd4c5ad51e76f51e
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://creativecommons.org/licenses/by/4.0/
dc.sourceUniversidad Nacional de Ingeniería
dc.sourceRepositorio Institucional - UNI
dc.subjectLocalization
dc.subjectMobile devices
dc.subjectSignal processing
dc.subjectModel fitting
dc.subjectWireless local area networks
dc.subjectSpatial analysis
dc.titleSpatial statistical analysis for the design of indoor particle-filter-based localization mechanisms
dc.typeArtículos de revistas


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