College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Sensors (Basel). 2018 Aug 24;18(9):2787. doi: 10.3390/s18092787.
The traditional speed prediction generally utilizes the Global Position System (GPS) and video images, and thus, the prediction precision mainly depends on environmental factors (i.e., weather, ionosphere, troposphere, air, and electromagnetic waves). We study the Radio Frequency Identification (RFID) data-driven vehicle speed prediction and proposed an improved extended kalman filter (i.e., the adaptive extended kalman filter, AEKF) algorithm. Firstly, the on-board RFID reader equipped in the vehicle reads the information (i.e., current speed and time) from the tag deployed on the road. Secondly, the received information is transmitted to the on-board information processing unit, and it is demodulated and decoded into available information. Finally, based on the vehicle state space model, the AEKF algorithm is proposed to predict vehicle speed and improve the prediction results, so that the vehicle speed gradually approaches the actual vehicle speed. The simulation results show that compared with the conventional extended kalman filter (EKF) algorithm, our proposed AEKF algorithm improves the dynamic performance of the filtering and better suppresses the filtering divergence process. Moreover, the AEKF algorithm also improves the precision of the Mean Square Error (MSE) and Mean Absolute Error (MAE) by 57.4% and 32.4%, respectively.
传统的速度预测通常利用全球定位系统 (GPS) 和视频图像，因此，预测精度主要取决于环境因素（即天气、电离层、对流层、空气和电磁波）。我们研究了基于射频识别 (RFID) 数据的车辆速度预测，并提出了一种改进的扩展卡尔曼滤波器（即自适应扩展卡尔曼滤波器，AEKF）算法。首先，车载 RFID 读取器从部署在道路上的标签读取信息（即当前速度和时间）。其次，接收到的信息被传输到车载信息处理单元，并对其进行解调和解码，以获得可用信息。最后，基于车辆状态空间模型，提出了 AEKF 算法来预测车辆速度，并改进预测结果，从而使车辆速度逐渐接近实际车辆速度。仿真结果表明，与传统的扩展卡尔曼滤波器 (EKF) 算法相比，我们提出的 AEKF 算法提高了滤波的动态性能，并更好地抑制了滤波发散过程。此外，AEKF 算法还分别将均方误差 (MSE) 和平均绝对误差 (MAE) 的精度提高了 57.4%和 32.4%。
