Tri-AoA: Robust AoA Estimation of Mobile RFID Tags With COTS Devices

Radio frequency identification (RFID) is a form of wireless communication that has received much attention in recent years due to low costs of passive RFID tags and availability of commercial-off-the-shelf (COTS) RFID devices. Existing indoor localization and tracking methods based on RFID do not perform well in dynamic environments with severe multi-path interference. In this paper, we propose a robust Angle of Arrival (AoA) estimation method for mobile RFID tags in a rich multi-path environment with a large feasible area. The proposed method Tri-AoA consists of three essential modules, phase likelihood estimation, Received Signal Strength Indicator (RSSI) likelihood estimation and a deep learning algorithm. The phase likelihood estimation module exploits the concept of an antenna array to provide a basic estimation of an AoA, but with an ambiguity. The RSSI likelihood estimation module helps alleviate the ambiguity. To achieve a more robust estimation of AoA for mobile RFID tags, we construct a 2-dimensional feature image that contains AoA estimation from the phase and RSSI modules. We then develop a deep learning algorithm to analyze this image to improve the AoA tracking accuracy as well as the robustness by suppressing the multi-path interference. The experimental results show that our system outperforms existing approaches by achieving a median error of $2.36^{\mathrm{o}}$ in a $3m\times 4m$ area using four COTS RFID antennas. We also show that our system can realize real-time performance on a personal computer.