Multi-antenna Array-based AoA Estimation Using Bluetooth Low Energy for Indoor Positioning
Shuai He, Hang Long, Wei Zhang
Abstract
In recent years, Bluetooth Low Energy (BLE) has developed into one of the key technologies for indoor positioning in the Internet of Things (IoT). Efficient positioning can be achieved by estimating the signal Angle of Arrival (AoA) of multi-antenna arrays to locate the device. AoA-based approaches are sensitive to noise, multipath, and path loss effects. Furthermore, AoA measurement using a multi-antenna array can also cause errors due to the receiver switching among single antennas. In this paper, an efficient signal processing method combined with nonlinear recursive least squares and unscented Kalman filter is proposed to solve the effect of noise, multipath and antenna switching. Experimental results show that compared with Multiple Signal Classification (MUSIC), the average estimation error of the AoA calculated by the proposed method is reduced by 3.9 degrees. The experiment illustrates the proposed method in the paper can effectively improve the accuracy of AoA.