Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms
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Otros documentos de la autoría: Bravenec, Tomáš; Gould, Michael; Fryza, Tomas; Torres-Sospedra, Joaquín
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Título
Influence of Measured Radio Map Interpolation on Indoor Positioning AlgorithmsFecha de publicación
2023-07-24Editor
IEEEISSN
1530-437XCita bibliográfica
T. Bravenec, M. Gould, T. Fryza and J. Torres-Sospedra, "Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms," in IEEE Sensors Journal, vol. 23, no. 17, pp. 20044-20054, 1 Sept.1, 2023Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/10192546Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal ... [+]
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements. [-]
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IEEE Sensors Journal, Volume 23, Issue 17 (September 2023)Entidad financiadora
European Comission
Proyecto de investigación
info:eu-repo/grantAgreement/EC/H2020/813278info:eu-repo/grantAgreement/EC/H2020/101023072
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