Multi-radio data fusion for indoor localization using bluetooth and WiFi

Location estimation through fusing the information obtainable from multiple radio systems can reduce the dependency on each system and improves the performance. Research on fusion-based indoor localization using WiFi and Bluetooth-low-energy (BLE) beacons has mostly been limited to training-based approaches. In this paper, we propose a training-free indoor localization technique using received signals from WiFi and BLE device. The proposed technique estimates the position of the user device by fusing the information that it gains regarding the position of the target from the WiFi channel state information (CSI) and the RSSI measurements of BLE beacons. We use the WiFi CSI to estimate the angle of arrival (AoA), which we then use in conjunction with the RSSI measurements from the BLE beacons to develop a multi-radio fusion framework for indoor localization. We use a weighted centroid localization method to obtain an initial position estimate from the RSSI measurements. The initial position estimation helps to resolve the ambiguities in the AoA. The proposed technique is based on maximum-likelihood estimation (MLE) exploiting the probability density functions of the estimated AoA and the RSSI-induced distances. Simulation results show that the proposed technique improves the localization accuracy by 30% in a typical indoor environment compared with previous approaches