In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs.

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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. <br>

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