Zhang Yunlei, Gong Xiaolin, Liu Kaihua, Zhang Shuai
School of Microelectronics, Tianjin University, Tianjin 300072, China.
Sensors (Basel). 2021 May 10;21(9):3286. doi: 10.3390/s21093286.
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.
基于最先进的射频识别（RFID）的室内自主车辆定位方法大多基于接收信号强度指示（RSSI）测量。然而，这些方法的精度在实际场景中还不够高。为了克服这个问题，开发了一种基于双频到达相位差（PDOA）测距的新型室内自主车辆定位与跟踪方案。首先，该方法通过双频PDOA测距获取RFID阅读器与标签之间的距离。然后，利用基于最大似然估计和半定规划（SDP）的定位算法来计算自主车辆的位置，该算法可以减轻多径测距误差并获得更准确的定位结果。最后，将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）进行融合。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实现了厘米级的平均跟踪精度。
