AoA and RSSI-Based BLE Indoor Positioning System With Kalman Filter and Data Fusion

This work aims at improving indoor positioning systems (IPS) by integrating multiple radio frequency techniques, namely received signal strength indiction (RSSI), Angle of Arrival (AoA), and a combination of both, within the bluetooth low energy (BLE) 5.1 framework. While AoA stands out for its precision, low energy consumption, and cost-effectiveness, RSSI is characterized by its simplicity and widespread availability. By resorting to a database of real RSSI and AoA measurements from a BLE 5.1 target node in a $14\times 8$ -m environment, our work employs the Kalman filter (KF) to improve the accuracy of multilateration, AoA combined with RSSI, and AoA-only algorithms. Moreover, we consider one more step in our IPS where the aforementioned KF-filtered outputs are then fused through a track fusion model. Results demonstrate that the proposed scheme, which we refer to as angle-RSSI fusion localization (ARFL), significantly improves localization accuracy compared to other techniques. In particular, it reduces up to 81.61% in the average position error when compared to multilateration with KF. This advanced IPS offers a cost-effective and precise solution suitable for various applications in industries, such as healthcare, commerce, and logistics.