Bluetooth Low Energy-based Angle of Arrival Estimation via Switch Antenna Array for Indoor Localization

With expected widespread implementation of 5G networks and 5G Internet of Things (IoT), indoor localization is expected to become of even further importance. Although Global Positioning System (GPS) ensures efficient outdoor localization, generally speaking, indoor localization systems fail to provide the same level of efficiency. In this regard, there has been recent widespread attention to Angle of Arrival (AoA) with the application on Switch Antenna Array (SAA), as an efficient indoor localization method due to its potential in determining location with low estimation error. The AoA, however, suffers from several issues including being sensitive to multipath effects, noise, fluctuations of received signal, and frequency/phase shifts. To tackle these issues, the paper proposes a set of signal processing and information fusion methods by integration of Nonlinear Least Square (NLS) curve fitting, Kalman Filter (KF), and Gaussian Filter (GF) to boost the accuracy rate of estimated angle. The proposed fusion framework is evaluated based on a real Bluetooth Low Energy (BLE) dataset and results illustrate significant potentials in terms of improving overall BLE-based achievable accuracy in angle detection.