State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

中文翻译：

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基于无源UHF RFID信号相位差的室内自动驾驶车定位和跟踪

基于最新射频识别（RFID）的室内自动驾驶汽车的定位方法主要基于接收信号强度指示器（RSSI）的测量。但是，这些方法的准确性对于实际情况而言还不够高。为了克服这个问题，开发了一种新颖的基于双频相位差到达（PDOA）测距的室内自主车辆定位和跟踪方案。首先，该方法通过双频PDOA测距获得RFID阅读器与标签之间的距离。然后，基于最大似然估计和基于半定规划（SDP）的定位算法来计算自动驾驶汽车的位置，从而可以减轻多径测距误差并获得更准确的定位结果。最后，车辆行驶信息和通过RFID定位获得的位置与卡尔曼滤波器（KF）融合在一起。所提出的方法可以在低密度标签部署环境中工作。仿真实验结果表明，所提出的车辆定位与跟踪方法达到了厘米级的平均跟踪精度。