Machine Learning calibration of Angle of Arrival methods based on different experimental Unified Linear and Rectified Array measurements

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.