期刊
IEEE ACCESS
卷 10, 期 -, 页码 123080-123093出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3223693
关键词
Bayesian Cramer-Rao lower bound; finger-printing; Gaussian process; indoor localization; magnetic field-based localization; particle filter
In this paper, the achievable position accuracy of magnetic localization is analyzed using Bayesian Cramer-Rao lower bounds, and deterministic inputs are accounted for. A Gaussian process is used to approximate the true analytical model based on training data. The results show that magnetic localization has a high potential accuracy.
In this paper, we show how to analyze the achievable position accuracy of magnetic localization based on Bayesian Cramer-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 10 cm indicating an high potential accuracy of magnetic localization.
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