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|Title:||Tri-AoA: robust AoA estimation of mobile RFID tags with COTS devices||Authors:||Wang, Zihao||Keywords:||Engineering::Electrical and electronic engineering::Electronic systems::Signal processing||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Wang, Z. (2022). Tri-AoA: robust AoA estimation of mobile RFID tags with COTS devices. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/161888||Abstract:||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° in a 3m * 4m area using four COTS RFID antennas. We also show that our system can realize real-time performance on a personal computer.||URI:||https://hdl.handle.net/10356/161888||Schools:||School of Electrical and Electronic Engineering||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Updated on Dec 8, 2023
Updated on Dec 8, 2023
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