Research Article
Speed Prediction of High Speed Mobile Vehicle Based on Extended Kalman Filter in RFID System
@INPROCEEDINGS{10.1007/978-3-030-00557-3_6, author={Yupin Huang and Liping Qian and Anqi Feng}, title={Speed Prediction of High Speed Mobile Vehicle Based on Extended Kalman Filter in RFID System}, proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings}, proceedings_a={MLICOM}, year={2018}, month={10}, keywords={Radio frequency identification Speed prediction Extended Kalman filter}, doi={10.1007/978-3-030-00557-3_6} }- Yupin Huang
Liping Qian
Anqi Feng
Year: 2018
Speed Prediction of High Speed Mobile Vehicle Based on Extended Kalman Filter in RFID System
MLICOM
Springer
DOI: 10.1007/978-3-030-00557-3_6
Abstract
The traditional speed prediction generally utilizes GPS and video images, and thus the prediction accuracy is heavily dependent on environmental factors. To this end, through using RFID (Radio Frequency Identification) data, this paper proposes a vehicle speed prediction algorithm based on Extended Kalman Filter (EKF). Specifically, the proposed algorithm works as follows. First, the RFID reader equipped in the vehicle acquires the state information of tags deployed on the road. Second, The data processing module equipped in the vehicle demodulation and decoding these information. At the same time, the RFID reader sends information to the RFID label after the current information is encoded and modulated. Third, the vehicle predicts the vehicle speed based on the EKF through establishing the state space model with acquired state data. The simulation results show that the proposed algorithm can effectively predict the vehicle speed at 0.6 s.
