Research Article
A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering
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@INPROCEEDINGS{10.1007/978-3-319-72823-0_55, author={Daxin Tian and Wenhao Liu and Xuting Duan and Hui Rong and Peng Guo and Wenyang Wang and Haijun Zhang}, title={A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering}, proceedings={5G for Future Wireless Networks. First International Conference, 5GWN 2017, Beijing, China, April 21-23, 2017, Proceedings}, proceedings_a={5GWN}, year={2018}, month={1}, keywords={Vehicular positioning enhancement Target vehicle (TV) GPS CV-IMM-EKF}, doi={10.1007/978-3-319-72823-0_55} }- Daxin Tian
Wenhao Liu
Xuting Duan
Hui Rong
Peng Guo
Wenyang Wang
Haijun Zhang
Year: 2018
A Vehicular Positioning Enhancement with Connected Vehicle Assistance Using Extended Kalman Filtering
5GWN
Springer
DOI: 10.1007/978-3-319-72823-0_55
Abstract
In this paper, we consider the problem of vehicular positioning enhancement with emerging connected vehicles (CV) technologies. In order to actually describe the scenario, the Interacting Multiple Model (IMM) filter is used for depicting varies of observation models. A CV-enhanced IMM filtering approach is proposed to locate a vehicle by data fusion from both coarse GPS data and the Doppler frequency shifts (DFS) measured from dedicated short-range communications (DSRC) radio signals. Simulation results state the effectiveness of the proposed approach called CV-IMM-EKF.
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