Bayesian Cramér-Rao Lower Bound for Magnetic Field-Based Localization

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Název: Bayesian Cramér-Rao Lower Bound for Magnetic Field-Based Localization
Autoři: Benjamin Siebler, Stephan Sand, Uwe D. Hanebeck
Zdroj: IEEE Access, Vol 10, Pp 123080-123093 (2022)
Informace o vydavateli: IEEE
Rok vydání: 2022
Sbírka: Directory of Open Access Journals: DOAJ Articles
Témata: Bayesian Cramér-Rao lower bound, finger-printing, Gaussian process, indoor localization, magnetic field-based localization, particle filter, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Popis: In this paper, we show how to analyze the achievable position accuracy of magnetic localization based on Bayesian Cramér-Rao lower bounds and how to account for deterministic inputs in the bound. The derivation of the bound requires an analytical model, e.g., a map or database, that links the position that is to be estimated to the corresponding magnetic field value. Unfortunately, finding an analytical model from the laws of physics is not feasible due to the complexity of the involved differential equations and the required knowledge about the environment. In this paper, we therefore use a Gaussian process (GP) that approximates the true analytical model based on training data. The GP ensures a smooth, differentiable likelihood and allows a strict Bayesian treatment of the estimation problem. Based on a novel set of measurements recorded in an indoor environment, the bound is evaluated for different sensor heights and is compared to the mean squared error of a particle filter. Furthermore, the bound is calculated for the case when only the magnetic magnitude is used for positioning and the case when the whole vector field is considered. For both cases, the resulting position bound is below 10cm indicating an high potential accuracy of magnetic localization.
Druh dokumentu: article in journal/newspaper
Jazyk: English
Relation: https://ieeexplore.ieee.org/document/9957005/; https://doaj.org/toc/2169-3536; https://doaj.org/article/77753935734549fca787c8a6e73380f3
DOI: 10.1109/ACCESS.2022.3223693
Dostupnost: https://doi.org/10.1109/ACCESS.2022.3223693
https://doaj.org/article/77753935734549fca787c8a6e73380f3
Přístupové číslo: edsbas.7E1E75FA
Databáze: BASE
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