Abstract
For numerous applications of the Internet-of-Things, localization is an essential element. However, due to technological constraints on these devices, standards methods of positioning, such as Global Navigation Satellite System or Time-of-Arrival methods, are not applicable. Therefore, Angle-of-Arrival (AoA) based localization is considered, using a densely deployed set of anchors equipped with arrays of antennas able to measure the Angle-of-Arrival of the signal emitted by the device to be located. The original method presented in this work consists in applying Polynomial Chaos Expansions to this problem in order to obtain statistical information on the position estimate of the device. To that end, it is assumed that the probability density functions of the AoA measurements are known at the anchors. Simulation results show that this method is able to closely approximate the confidence region of the device position.
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Acknowledgements
This work was supported by F.R.S-FNRS, and by Innoviris through the Copine-IoT project.
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Van der Vorst, T. et al. (2019). Application of Polynomial Chaos Expansions for Uncertainty Estimation in Angle-of-Arrival Based Localization. In: Canavero, F. (eds) Uncertainty Modeling for Engineering Applications. PoliTO Springer Series. Springer, Cham. https://doi.org/10.1007/978-3-030-04870-9_7
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DOI: https://doi.org/10.1007/978-3-030-04870-9_7
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