Pan Ruoyu, Han Zhao, Liu Tuo, Wang Honggang, Huang Jinyue, Wang Wenfeng
School of Communications and Information Engineering and School of Artificial Intelligence, Xi'an University of Posts and Telecommunications, Xi'an 710121, China.
School of Science, Xi'an Shiyou University, Xi'an 710065, China.
Sensors (Basel). 2023 Aug 7;23(15):7001. doi: 10.3390/s23157001.
Intelligent transportation systems (ITS) urgently need to realize vehicle identification, dynamic monitoring, and traffic flow monitoring under high-speed motion conditions. Vehicle tracking based on radio frequency identification (RFID) and electronic vehicle identification (EVI) can obtain continuous observation data for a long period of time, and the acquisition accuracy is relatively high, which is conducive to the discovery of rules. The data can provide key information for urban traffic decision-making research. In this paper, an RFID tag motion trajectory tracking method based on RF multiple features for ITS is proposed to analyze the movement trajectory of vehicles at important checkpoints. The method analyzes the accurate relationship between the RSSI, phase differences, and driving distances of the tag. It utilizes the information weight method to obtain the weights of multiple RF characteristics at different distances. Then, it calculates the center point of the common area where the vehicle may move under multi-antenna conditions, confirming the actual position of the vehicle. The experimental results show that the average positioning error of moving RFID tags based on dual-frequency signal phase differences and RSSI is less than 17 cm. This method can provide real-time, high-precision vehicle positioning and trajectory tracking solutions for ITS application scenarios such as parking guidance, unmanned vehicle route monitoring, and vehicle lane change detection.
智能交通系统（ITS）迫切需要在高速运动条件下实现车辆识别、动态监测和交通流量监测。基于射频识别（RFID）和电子车辆识别（EVI）的车辆跟踪可以长时间获取连续的观测数据，且采集精度相对较高，有利于发现规律。这些数据可为城市交通决策研究提供关键信息。本文提出一种基于射频多特征的ITS射频识别标签运动轨迹跟踪方法，用于分析车辆在重要检查点的运动轨迹。该方法分析标签的接收信号强度指示（RSSI）、相位差和行驶距离之间的精确关系。利用信息权重法获取不同距离下多个射频特征的权重。然后，计算多天线条件下车辆可能移动的公共区域的中心点，确定车辆的实际位置。实验结果表明，基于双频信号相位差和RSSI的移动射频识别标签的平均定位误差小于17厘米。该方法可为停车引导、无人驾驶车辆路线监测和车辆变道检测等ITS应用场景提供实时、高精度的车辆定位和轨迹跟踪解决方案。
