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.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Project 61379122, Project 61572440, and Project 61502428, and in part by the Zhejiang Provincial Natural Science Foundation of China under Project LR16F010003, and Project LR17F010002.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Huang, Y., Qian, L., Feng, A. (2018). Speed Prediction of High Speed Mobile Vehicle Based on Extended Kalman Filter in RFID System. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_6
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DOI: https://doi.org/10.1007/978-3-030-00557-3_6
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