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Machine Learning calibration of Angle of Arrival methods based on different experimental Unified Linear and Rectified Array measurements
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  • Timon Merk ,
  • Mohamad Abou Nasa ,
  • Farshid Rezai ,
  • Peter Karlsson ,
  • Matthias Mahlig
Timon Merk
u-blox Berlin GmbH

Corresponding Author:[email protected]

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Mohamad Abou Nasa
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Farshid Rezai
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Peter Karlsson
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Matthias Mahlig
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Abstract

Generic Angle of Arrival methods for indoor positioning are highly affected by specific antenna and environment scenarios through design impurities or multipathcomponent propagations. Here we acquired a large dataset of four different antenna designs in three different measurement environments with >140000 snapshots obtained from Bluetooth 5.1 receiver. Using the spatial power spectral densities of the PDDA angle of arrival algorithm as feature set for a small Random Forest model, we could show that angle estimation performances for all antennas in all measured environments were significantly improved (PDDA MAE >16 vs RF MAE < 3). Based on the small model size the proposed architecture can be implemented in microcontroller applications for super resolution angle of arrival applications.